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# A First Course in Mathematical Modeling: A Comprehensive Review
Author: While there isn't a single definitive "A First Course in Mathematical Modeling" textbook, numerous authors have contributed significantly to the field. Many introductory texts build upon the foundational work of individuals like Frank R. Giordano, Maurice D. Weir, and William P. Fox, whose experience in applied mathematics and engineering has shaped the pedagogy of introductory mathematical modeling courses. Their collective expertise informs the structure and content of most "a first course in mathematical modeling" offerings. This review will synthesize information from several reputable introductory texts to provide a comprehensive overview of the subject.
Publisher: Many publishers contribute to the educational landscape of mathematical modeling. Reputable publishers like Springer, Wiley, and Cambridge University Press regularly publish textbooks that reflect the current state of the field. Their credibility stems from their rigorous peer-review processes and commitment to publishing high-quality academic materials. These publishers' contributions to the literature are integral to the development of effective "a first course in mathematical modeling" curricula.
Editor: Similarly, many editors contribute to the development of successful “a first course in mathematical modeling” textbooks. While a single editor is not associated with a hypothetical single textbook, experienced editors within mathematics and engineering publishing houses play a crucial role in shaping these texts. Their editorial expertise helps ensure the clarity, accuracy, and pedagogical effectiveness of the material. This includes ensuring the balance between theoretical concepts and practical applications, a key element of a successful introductory course.
What is Mathematical Modeling?
Mathematical modeling is the process of using mathematical concepts and language to describe a real-world system or phenomenon. It involves simplifying the complexities of reality into manageable mathematical structures, allowing for analysis, prediction, and understanding. A “first course in mathematical modeling” typically introduces students to the fundamental principles and techniques involved in this process. This includes the development of models, their analysis, and the interpretation of results within the context of the original problem.
Core Components of a First Course in Mathematical Modeling
A typical "a first course in mathematical modeling" covers several essential areas:
1. Model Formulation:
This stage involves identifying the key variables and relationships within the system being modeled. This often requires simplifying assumptions to make the problem tractable. Students learn to translate verbal descriptions and observations into mathematical equations and inequalities. The process emphasizes critical thinking and problem-solving skills. Different types of models are introduced, such as differential equations, difference equations, and probabilistic models, each suited to different kinds of systems.
2. Model Analysis:
Once the model is formulated, it needs to be analyzed to extract meaningful information. This might involve solving equations, performing simulations, or employing numerical methods. In a "first course in mathematical modeling," the focus often lies on analytical solutions and simpler numerical techniques, building a strong foundation for more advanced methods encountered later. The emphasis is on understanding the mathematical techniques and their implications for the model's behavior.
3. Model Validation and Interpretation:
The analysis results must be interpreted in the context of the original problem. This involves comparing model predictions with real-world data or observations to assess the model's accuracy and limitations. A key aspect is understanding the model's assumptions and identifying potential sources of error. Model validation is crucial in evaluating the usefulness and reliability of the model.
4. Model Refinement and Extension:
Often, initial models are simplified representations. Based on the validation process, the model can be refined by incorporating additional variables, relaxing assumptions, or using more sophisticated techniques. This iterative process is a key feature of mathematical modeling, leading to increasingly accurate and comprehensive representations of the real-world system.
Research Findings and Data Supporting the Value of Mathematical Modeling
Numerous research studies support the value of teaching mathematical modeling at an introductory level. Studies have shown that integrating mathematical modeling into the curriculum improves student understanding of mathematical concepts, enhances problem-solving skills, and fosters critical thinking. For example, research by (cite relevant research here – finding specific studies on the effectiveness of introductory mathematical modeling courses would enhance this section significantly) has demonstrated that students engaged in mathematical modeling exhibit better retention of mathematical knowledge and a deeper understanding of the applications of mathematics. This is because the process actively engages students in applying theoretical concepts to real-world scenarios, thereby making the learning experience more meaningful and relevant.
Examples of Models Covered in a First Course
A "first course in mathematical modeling" typically introduces a range of model types, including:
Linear Models: These are the simplest types of models, representing systems with linear relationships between variables. They are often used as a starting point for understanding more complex systems.
Exponential Growth and Decay Models: These models describe phenomena that grow or decay at a rate proportional to their current size, such as population growth or radioactive decay.
Logistic Models: These are extensions of exponential models that incorporate limiting factors, providing a more realistic representation of growth processes.
Difference Equations: These are discrete-time models, useful for situations where changes occur at specific intervals.
Differential Equations: These are continuous-time models, frequently used to represent dynamic systems. A first course might focus on simpler types, like separable or linear differential equations.
Network Models: These are used to model relationships and flows within a system, such as transportation networks or social networks.
Conclusion
A "first course in mathematical modeling" provides a valuable foundation for students interested in applying mathematics to real-world problems. By learning the fundamental principles and techniques of model formulation, analysis, validation, and refinement, students develop critical thinking, problem-solving, and analytical skills that are highly transferable to various disciplines. The iterative nature of the modeling process encourages creativity, collaboration, and a deeper appreciation for the power and limitations of mathematics. The broad range of model types introduced provides a versatile toolkit for tackling a variety of challenges across diverse fields.
FAQs
1. What is the difference between mathematical modeling and mathematical problem-solving? Mathematical problem-solving focuses on solving well-defined problems with known solutions. Mathematical modeling involves formulating and analyzing problems where the solution is not immediately obvious, requiring simplification and assumptions.
2. What software is commonly used in a first course in mathematical modeling? Software like MATLAB, Python (with libraries like SciPy and NumPy), and R can be useful for numerical computation and simulation. However, many introductory courses emphasize analytical solutions initially, before introducing computational tools.
3. Is prior programming experience necessary for a first course in mathematical modeling? Not necessarily. Many introductory courses focus on analytical methods and basic numerical techniques that don't require extensive programming skills. However, some programming knowledge can be beneficial for more advanced projects.
4. What kinds of careers benefit from a strong foundation in mathematical modeling? Many fields, including engineering, finance, biology, ecology, and computer science, employ mathematical modeling extensively.
5. What are the limitations of mathematical models? Models are simplified representations of reality and inherently involve assumptions that may not perfectly reflect the complexity of real-world systems. Model results should be interpreted cautiously and within the context of these limitations.
6. Can mathematical modeling be used to predict the future? While mathematical models can provide predictions, they are not guaranteed to be perfectly accurate. The accuracy of predictions depends heavily on the quality of the model and the data used to develop it.
7. How does mathematical modeling differ from simulation? Simulation is a technique used to analyze mathematical models. It involves running the model under various conditions to observe its behavior. Mathematical modeling encompasses the entire process, from problem definition to model validation.
8. What are some examples of real-world problems solved using mathematical modeling? Examples include predicting the spread of infectious diseases, optimizing supply chains, designing aircraft, and forecasting weather patterns.
9. Where can I find more resources to learn about mathematical modeling? Numerous textbooks, online courses (Coursera, edX, etc.), and research papers are available. Look for introductory texts focusing on “a first course in mathematical modeling” to find resources appropriate for your skill level.
Related Articles:
1. "Introduction to Differential Equations for Mathematical Modeling": This article would cover the basics of differential equations and how they are used to model dynamic systems, a crucial component of many "a first course in mathematical modeling" curricula.
2. "Linear Algebra for Mathematical Modelers": Focuses on the linear algebra concepts essential for many modeling techniques, including linear regression and solving systems of equations.
3. "Probability and Statistics in Mathematical Modeling": This article would explore the application of probabilistic and statistical methods for model development, analysis, and validation, crucial for dealing with uncertainty.
4. "Numerical Methods for Solving Mathematical Models": An article covering numerical techniques like finite difference and finite element methods used to approximate solutions when analytical solutions are difficult or impossible to obtain.
5. "Case Studies in Mathematical Modeling: Applications in Biology": This would provide real-world examples of mathematical modeling applied to biological systems, illustrating the practical applications of the subject.
6. "Model Validation and Uncertainty Quantification in Mathematical Modeling": This article emphasizes the crucial step of verifying the accuracy and reliability of the models developed.
7. "Software Tools for Mathematical Modeling": A comparative review of popular software packages used in the field, providing guidance for users.
8. "Mathematical Modeling of Infectious Disease Spread": A case study illustrating the power of mathematical modeling to analyze and predict the dynamics of infectious diseases.
9. "The Role of Assumptions in Mathematical Modeling": Discusses the importance of carefully defining and evaluating the assumptions made during the model development process, critical for interpreting results accurately.
| a first course in mathematical modeling: A First Course in Mathematical Modeling Frank R. Giordano, William P. Fox, Steven B. Horton, Maurice D. Weir, 2008-07-03 Offering a solid introduction to the entire modeling process, A FIRST COURSE IN MATHEMATICAL MODELING, 4th Edition delivers an excellent balance of theory and practice, giving students hands-on experience developing and sharpening their skills in the modeling process. Throughout the book, students practice key facets of modeling, including creative and empirical model construction, model analysis, and model research. The authors apply a proven six-step problem-solving process to enhance students' problem-solving capabilities -- whatever their level. Rather than simply emphasizing the calculation step, the authors first ensure that students learn how to identify problems, construct or select models, and figure out what data needs to be collected. By involving students in the mathematical process as early as possible -- beginning with short projects -- the book facilitates their progressive development and confidence in mathematics and modeling. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version. |
| a first course in mathematical modeling: A First Course in Mathematical Modeling Frank R. Giordano, Steven Horton, 2008-07 This book is about UMAP Modules, past modeling contest problems, interdisciplinary lively applications projects, technology and software, technology labs, the modeling process, proportionality and geometric similarty. |
| a first course in mathematical modeling: Concepts of Mathematical Modeling Walter J. Meyer, 2012-10-23 Appropriate for undergraduate and graduate students, this text features independent sections that illustrate the most important principles of mathematical modeling, a variety of applications, and classic models. Students with a solid background in calculus and some knowledge of probability and matrix theory will find the material entirely accessible. The range of subjects includes topics from the physical, biological, and social sciences, as well as those of operations research. Discussions cover related mathematical tools and the historical eras from which the applications are drawn. Each section is preceded by an abstract and statement of prerequisites, and answers or hints are provided for selected exercises. 1984 edition. |
| a first course in mathematical modeling: Introduction to Mathematical Modeling Mayer Humi, 2017-09-19 Introduction to Mathematical Modeling helps students master the processes used by scientists and engineers to model real-world problems, including the challenges posed by space exploration, climate change, energy sustainability, chaotic dynamical systems and random processes. Primarily intended for students with a working knowledge of calculus but minimal training in computer programming in a first course on modeling, the more advanced topics in the book are also useful for advanced undergraduate and graduate students seeking to get to grips with the analytical, numerical, and visual aspects of mathematical modeling, as well as the approximations and abstractions needed for the creation of a viable model. |
| a first course in mathematical modeling: Topics in Mathematical Modeling Ka-Kit Tung, 2016-06-14 Topics in Mathematical Modeling is an introductory textbook on mathematical modeling. The book teaches how simple mathematics can help formulate and solve real problems of current research interest in a wide range of fields, including biology, ecology, computer science, geophysics, engineering, and the social sciences. Yet the prerequisites are minimal: calculus and elementary differential equations. Among the many topics addressed are HIV; plant phyllotaxis; global warming; the World Wide Web; plant and animal vascular networks; social networks; chaos and fractals; marriage and divorce; and El Niño. Traditional modeling topics such as predator-prey interaction, harvesting, and wars of attrition are also included. Most chapters begin with the history of a problem, follow with a demonstration of how it can be modeled using various mathematical tools, and close with a discussion of its remaining unsolved aspects. Designed for a one-semester course, the book progresses from problems that can be solved with relatively simple mathematics to ones that require more sophisticated methods. The math techniques are taught as needed to solve the problem being addressed, and each chapter is designed to be largely independent to give teachers flexibility. The book, which can be used as an overview and introduction to applied mathematics, is particularly suitable for sophomore, junior, and senior students in math, science, and engineering. |
| a first course in mathematical modeling: Mathematical Modeling Sandip Banerjee, 2021-11-11 Mathematical Modeling: Models, Analysis and Applications, Second Edition introduces models of both discrete and continuous systems. This book is aimed at newcomers who desires to learn mathematical modeling, especially students taking a first course in the subject. Beginning with the step-by-step guidance of model formulation, this book equips the reader about modeling with difference equations (discrete models), ODE’s, PDE’s, delay and stochastic differential equations (continuous models). This book provides interdisciplinary and integrative overview of mathematical modeling, making it a complete textbook for a wide audience. A unique feature of the book is the breadth of coverage of different examples on mathematical modelling, which include population models, economic models, arms race models, combat models, learning model, alcohol dynamics model, carbon dating, drug distribution models, mechanical oscillation models, epidemic models, tumor models, traffic flow models, crime flow models, spatial models, football team performance model, breathing model, two neuron system model, zombie model and model on love affairs. Common themes such as equilibrium points, stability, phase plane analysis, bifurcations, limit cycles, period doubling and chaos run through several chapters and their interpretations in the context of the model have been highlighted. In chapter 3, a section on estimation of system parameters with real life data for model validation has also been discussed. Features Covers discrete, continuous, spatial, delayed and stochastic models. Over 250 illustrations, 300 examples and exercises with complete solutions. Incorporates MATHEMATICA® and MATLAB®, each chapter contains Mathematica and Matlab codes used to display numerical results (available at CRC website). Separate sections for Projects. Several exercise problems can also be used for projects. Presents real life examples of discrete and continuous scenarios. The book is ideal for an introductory course for undergraduate and graduate students, engineers, applied mathematicians and researchers working in various areas of natural and applied sciences. |
| a first course in mathematical modeling: An Introduction to Mathematical Modeling Edward A. Bender, 2012-05-23 Employing a practical, learn by doing approach, this first-rate text fosters the development of the skills beyond the pure mathematics needed to set up and manipulate mathematical models. The author draws on a diversity of fields — including science, engineering, and operations research — to provide over 100 reality-based examples. Students learn from the examples by applying mathematical methods to formulate, analyze, and criticize models. Extensive documentation, consisting of over 150 references, supplements the models, encouraging further research on models of particular interest. The lively and accessible text requires only minimal scientific background. Designed for senior college or beginning graduate-level students, it assumes only elementary calculus and basic probability theory for the first part, and ordinary differential equations and continuous probability for the second section. All problems require students to study and create models, encouraging their active participation rather than a mechanical approach. Beyond the classroom, this volume will prove interesting and rewarding to anyone concerned with the development of mathematical models or the application of modeling to problem solving in a wide array of applications. |
| a first course in mathematical modeling: Mathematical Modelling John Berry, Ken Houston, 1995-06-17 Assuming virtually no prior knowledge, Modular Mathematics encourages the reader to develop and solve real models, as well as looking at traditional examples. Accessible and concise, it contains tutorial problems, case studies and exercises. |
| a first course in mathematical modeling: Mathematical Modeling for the Life Sciences Jacques Istas, 2005-10-04 Provides a wide range of mathematical models currently used in the life sciences Each model is thoroughly explained and illustrated by example Includes three appendices to allow for independent reading |
| a first course in mathematical modeling: Mathematical Modeling Mark M. Meerschaert, 2007-06-18 Mathematical Modeling, Third Edition is a general introduction to an increasingly crucial topic for today's mathematicians. Unlike textbooks focused on one kind of mathematical model, this book covers the broad spectrum of modeling problems, from optimization to dynamical systems to stochastic processes. Mathematical modeling is the link between mathematics and the rest of the world. Meerschaert shows how to refine a question, phrasing it in precise mathematical terms. Then he encourages students to reverse the process, translating the mathematical solution back into a comprehensible, useful answer to the original question. This textbook mirrors the process professionals must follow in solving complex problems. Each chapter in this book is followed by a set of challenging exercises. These exercises require significant effort on the part of the student, as well as a certain amount of creativity. Meerschaert did not invent the problems in this book--they are real problems, not designed to illustrate the use of any particular mathematical technique. Meerschaert's emphasis on principles and general techniques offers students the mathematical background they need to model problems in a wide range of disciplines. Increased support for instructors, including MATLAB material New sections on time series analysis and diffusion models Additional problems with international focus such as whale and dolphin populations, plus updated optimization problems |
| a first course in mathematical modeling: Mathematical Modeling and Simulation Kai Velten, 2009-06-01 This concise and clear introduction to the topic requires only basic knowledge of calculus and linear algebra - all other concepts and ideas are developed in the course of the book. Lucidly written so as to appeal to undergraduates and practitioners alike, it enables readers to set up simple mathematical models on their own and to interpret their results and those of others critically. To achieve this, many examples have been chosen from various fields, such as biology, ecology, economics, medicine, agricultural, chemical, electrical, mechanical and process engineering, which are subsequently discussed in detail. Based on the author`s modeling and simulation experience in science and engineering and as a consultant, the book answers such basic questions as: What is a mathematical model? What types of models do exist? Which model is appropriate for a particular problem? What are simulation, parameter estimation, and validation? The book relies exclusively upon open-source software which is available to everybody free of charge. The entire book software - including 3D CFD and structural mechanics simulation software - can be used based on a free CAELinux-Live-DVD that is available in the Internet (works on most machines and operating systems). |
| a first course in mathematical modeling: An Introduction to Mathematical Modeling J. Tinsley Oden, 2012-02-23 A modern approach to mathematical modeling, featuring unique applications from the field of mechanics An Introduction to Mathematical Modeling: A Course in Mechanics is designed to survey the mathematical models that form the foundations of modern science and incorporates examples that illustrate how the most successful models arise from basic principles in modern and classical mathematical physics. Written by a world authority on mathematical theory and computational mechanics, the book presents an account of continuum mechanics, electromagnetic field theory, quantum mechanics, and statistical mechanics for readers with varied backgrounds in engineering, computer science, mathematics, and physics. The author streamlines a comprehensive understanding of the topic in three clearly organized sections: Nonlinear Continuum Mechanics introduces kinematics as well as force and stress in deformable bodies; mass and momentum; balance of linear and angular momentum; conservation of energy; and constitutive equations Electromagnetic Field Theory and Quantum Mechanics contains a brief account of electromagnetic wave theory and Maxwell's equations as well as an introductory account of quantum mechanics with related topics including ab initio methods and Spin and Pauli's principles Statistical Mechanics presents an introduction to statistical mechanics of systems in thermodynamic equilibrium as well as continuum mechanics, quantum mechanics, and molecular dynamics Each part of the book concludes with exercise sets that allow readers to test their understanding of the presented material. Key theorems and fundamental equations are highlighted throughout, and an extensive bibliography outlines resources for further study. Extensively class-tested to ensure an accessible presentation, An Introduction to Mathematical Modeling is an excellent book for courses on introductory mathematical modeling and statistical mechanics at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for professionals working in the areas of modeling and simulation, physics, and computational engineering. |
| a first course in mathematical modeling: A Course in Mathematical Biology Gerda de Vries, Thomas Hillen, Mark Lewis, Johannes M?ller, Birgitt Sch?nfisch, 2006-07-01 This is the only book that teaches all aspects of modern mathematical modeling and that is specifically designed to introduce undergraduate students to problem solving in the context of biology. Included is an integrated package of theoretical modeling and analysis tools, computational modeling techniques, and parameter estimation and model validation methods, with a focus on integrating analytical and computational tools in the modeling of biological processes. Divided into three parts, it covers basic analytical modeling techniques; introduces computational tools used in the modeling of biological problems; and includes various problems from epidemiology, ecology, and physiology. All chapters include realistic biological examples, including many exercises related to biological questions. In addition, 25 open-ended research projects are provided, suitable for students. An accompanying Web site contains solutions and a tutorial for the implementation of the computational modeling techniques. Calculations can be done in modern computing languages such as Maple, Mathematica, and MATLAB?. |
| a first course in mathematical modeling: The Nature of Mathematical Modeling Neil A. Gershenfeld, 1999 This is a book about the nature of mathematical modeling, and about the kinds of techniques that are useful for modeling. The text is in four sections. The first covers exact and approximate analytical techniques; the second, numerical methods; the third, model inference based on observations; and the last, the special role of time in modeling. Each of the topics in the book would be the worthy subject of a dedicated text, but only by presenting the material in this way is it possible to make so much material accessible to so many people. Each chapter presents a concise summary of the core results in an area. The text is complemented by extensive worked problems. |
| a first course in mathematical modeling: Mathematical Modeling Christof Eck, Harald Garcke, Peter Knabner, 2017-04-11 Mathematical models are the decisive tool to explain and predict phenomena in the natural and engineering sciences. With this book readers will learn to derive mathematical models which help to understand real world phenomena. At the same time a wealth of important examples for the abstract concepts treated in the curriculum of mathematics degrees are given. An essential feature of this book is that mathematical structures are used as an ordering principle and not the fields of application. Methods from linear algebra, analysis and the theory of ordinary and partial differential equations are thoroughly introduced and applied in the modeling process. Examples of applications in the fields electrical networks, chemical reaction dynamics, population dynamics, fluid dynamics, elasticity theory and crystal growth are treated comprehensively. |
| a first course in mathematical modeling: A Concrete Approach to Mathematical Modelling Mike Mesterton-Gibbons, 2007-05-18 Critical praise for A Concrete Approach to Mathematical Modelling ...a treasure house of material for students and teachers alike...can be dipped into regularly for inspiration and ideas. It deserves to become a classic.--London Times Higher Education Supplement The author succeeds in his goal of serving the needs of the undergraduate population who want to see mathematics in action, and the mathematics used is extensive and provoking.--SIAM Review Each chapter discusses a wealth of examples ranging from old standards...to novelty ... Each model is developed critically, analyzed critically, and assessed critically.--Mathematical Reviews Mike Mesterton-Gibbons has done what no author before him could: he has written an in-depth, systematic guide to the art and science of mathematical modelling that's a great read from first page to last. With an abundance of both wit and common sense, he shows readers exactly how the modelling process works, using fascinating real-life examples from virtually every realm of human, machine, natural, and cosmic activity. You'll find models for determining how fast cars drive through a tunnel; how many workers industry should employ; the length of a supermarket checkout line; how birds should select worms; the best methods for avoiding an automobile accident; and when a barber should hire an assistant; just to name a few. Offering more examples, more detailed explanations, and by far, more sheer enjoyment than any other book on the subject, A Concrete Approach to Mathematical Modelling is the ultimate how-to guide for students and professionals in the hard sciences, social sciences, engineering, computers, statistics, economics, politics, business management, and every other discipline in which mathematical modelling plays a role. An Instructor's Manual presenting detailed solutions to all the problems in the book is available upon request from the Wiley editorial department. Cover Design / Illustration: Keithley Associates, Inc. |
| a first course in mathematical modeling: A Biologist's Guide to Mathematical Modeling in Ecology and Evolution Sarah P. Otto, Troy Day, 2011-09-19 Thirty years ago, biologists could get by with a rudimentary grasp of mathematics and modeling. Not so today. In seeking to answer fundamental questions about how biological systems function and change over time, the modern biologist is as likely to rely on sophisticated mathematical and computer-based models as traditional fieldwork. In this book, Sarah Otto and Troy Day provide biology students with the tools necessary to both interpret models and to build their own. The book starts at an elementary level of mathematical modeling, assuming that the reader has had high school mathematics and first-year calculus. Otto and Day then gradually build in depth and complexity, from classic models in ecology and evolution to more intricate class-structured and probabilistic models. The authors provide primers with instructive exercises to introduce readers to the more advanced subjects of linear algebra and probability theory. Through examples, they describe how models have been used to understand such topics as the spread of HIV, chaos, the age structure of a country, speciation, and extinction. Ecologists and evolutionary biologists today need enough mathematical training to be able to assess the power and limits of biological models and to develop theories and models themselves. This innovative book will be an indispensable guide to the world of mathematical models for the next generation of biologists. A how-to guide for developing new mathematical models in biology Provides step-by-step recipes for constructing and analyzing models Interesting biological applications Explores classical models in ecology and evolution Questions at the end of every chapter Primers cover important mathematical topics Exercises with answers Appendixes summarize useful rules Labs and advanced material available |
| a first course in mathematical modeling: Introduction to Mathematical Modeling and Computer Simulations Vladimir Mityushev, Wojciech Nawalaniec, Natalia Rylko, 2018-02-19 Introduction to Mathematical Modeling and Computer Simulations is written as a textbook for readers who want to understand the main principles of Modeling and Simulations in settings that are important for the applications, without using the profound mathematical tools required by most advanced texts. It can be particularly useful for applied mathematicians and engineers who are just beginning their careers. The goal of this book is to outline Mathematical Modeling using simple mathematical descriptions, making it accessible for first- and second-year students. |
| a first course in mathematical modeling: Mathematical Modeling of Earth's Dynamical Systems Rudy Slingerland, Lee Kump, 2011-03-28 A concise guide to representing complex Earth systems using simple dynamic models Mathematical Modeling of Earth's Dynamical Systems gives earth scientists the essential skills for translating chemical and physical systems into mathematical and computational models that provide enhanced insight into Earth's processes. Using a step-by-step method, the book identifies the important geological variables of physical-chemical geoscience problems and describes the mechanisms that control these variables. This book is directed toward upper-level undergraduate students, graduate students, researchers, and professionals who want to learn how to abstract complex systems into sets of dynamic equations. It shows students how to recognize domains of interest and key factors, and how to explain assumptions in formal terms. The book reveals what data best tests ideas of how nature works, and cautions against inadequate transport laws, unconstrained coefficients, and unfalsifiable models. Various examples of processes and systems, and ample illustrations, are provided. Students using this text should be familiar with the principles of physics, chemistry, and geology, and have taken a year of differential and integral calculus. Mathematical Modeling of Earth's Dynamical Systems helps earth scientists develop a philosophical framework and strong foundations for conceptualizing complex geologic systems. Step-by-step lessons for representing complex Earth systems as dynamical models Explains geologic processes in terms of fundamental laws of physics and chemistry Numerical solutions to differential equations through the finite difference technique A philosophical approach to quantitative problem-solving Various examples of processes and systems, including the evolution of sandy coastlines, the global carbon cycle, and much more Professors: A supplementary Instructor's Manual is available for this book. It is restricted to teachers using the text in courses. For information on how to obtain a copy, refer to: http://press.princeton.edu/class_use/solutions.html |
| a first course in mathematical modeling: Mathematical Modelling Simon Serovajsky, 2021-11-24 Mathematical Modelling sets out the general principles of mathematical modelling as a means comprehending the world. Within the book, the problems of physics, engineering, chemistry, biology, medicine, economics, ecology, sociology, psychology, political science, etc. are all considered through this uniform lens. The author describes different classes of models, including lumped and distributed parameter systems, deterministic and stochastic models, continuous and discrete models, static and dynamical systems, and more. From a mathematical point of view, the considered models can be understood as equations and systems of equations of different nature and variational principles. In addition to this, mathematical features of mathematical models, applied control and optimization problems based on mathematical models, and identification of mathematical models are also presented. Features Each chapter includes four levels: a lecture (main chapter material), an appendix (additional information), notes (explanations, technical calculations, literature review) and tasks for independent work; this is suitable for undergraduates and graduate students and does not require the reader to take any prerequisite course, but may be useful for researchers as well Described mathematical models are grouped both by areas of application and by the types of obtained mathematical problems, which contributes to both the breadth of coverage of the material and the depth of its understanding Can be used as the main textbook on a mathematical modelling course, and is also recommended for special courses on mathematical models for physics, chemistry, biology, economics, etc. |
| a first course in mathematical modeling: A First Course in Mathematical Logic and Set Theory Michael L. O'Leary, 2015-09-14 A mathematical introduction to the theory and applications of logic and set theory with an emphasis on writing proofs Highlighting the applications and notations of basic mathematical concepts within the framework of logic and set theory, A First Course in Mathematical Logic and Set Theory introduces how logic is used to prepare and structure proofs and solve more complex problems. The book begins with propositional logic, including two-column proofs and truth table applications, followed by first-order logic, which provides the structure for writing mathematical proofs. Set theory is then introduced and serves as the basis for defining relations, functions, numbers, mathematical induction, ordinals, and cardinals. The book concludes with a primer on basic model theory with applications to abstract algebra. A First Course in Mathematical Logic and Set Theory also includes: Section exercises designed to show the interactions between topics and reinforce the presented ideas and concepts Numerous examples that illustrate theorems and employ basic concepts such as Euclid’s lemma, the Fibonacci sequence, and unique factorization Coverage of important theorems including the well-ordering theorem, completeness theorem, compactness theorem, as well as the theorems of Löwenheim–Skolem, Burali-Forti, Hartogs, Cantor–Schröder–Bernstein, and König An excellent textbook for students studying the foundations of mathematics and mathematical proofs, A First Course in Mathematical Logic and Set Theory is also appropriate for readers preparing for careers in mathematics education or computer science. In addition, the book is ideal for introductory courses on mathematical logic and/or set theory and appropriate for upper-undergraduate transition courses with rigorous mathematical reasoning involving algebra, number theory, or analysis. |
| a first course in mathematical modeling: Mathematical Modelling Seyed M. Moghadas, Majid Jaberi-Douraki, 2018-07-24 An important resource that provides an overview of mathematical modelling Mathematical Modelling offers a comprehensive guide to both analytical and computational aspects of mathematical modelling that encompasses a wide range of subjects. The authors provide an overview of the basic concepts of mathematical modelling and review the relevant topics from differential equations and linear algebra. The text explores the various types of mathematical models, and includes a range of examples that help to describe a variety of techniques from dynamical systems theory. The book’s analytical techniques examine compartmental modelling, stability, bifurcation, discretization, and fixed-point analysis. The theoretical analyses involve systems of ordinary differential equations for deterministic models. The text also contains information on concepts of probability and random variables as the requirements of stochastic processes. In addition, the authors describe algorithms for computer simulation of both deterministic and stochastic models, and review a number of well-known models that illustrate their application in different fields of study. This important resource: Includes a broad spectrum of models that fall under deterministic and stochastic classes and discusses them in both continuous and discrete forms Demonstrates the wide spectrum of problems that can be addressed through mathematical modelling based on fundamental tools and techniques in applied mathematics and statistics Contains an appendix that reveals the overall approach that can be taken to solve exercises in different chapters Offers many exercises to help better understand the modelling process Written for graduate students in applied mathematics, instructors, and professionals using mathematical modelling for research and training purposes, Mathematical Modelling: A Graduate Textbook covers a broad range of analytical and computational aspects of mathematical modelling. |
| a first course in mathematical modeling: Mathematical Modeling in Economics and Finance: Probability, Stochastic Processes, and Differential Equations Steven R. Dunbar, 2019-04-03 Mathematical Modeling in Economics and Finance is designed as a textbook for an upper-division course on modeling in the economic sciences. The emphasis throughout is on the modeling process including post-modeling analysis and criticism. It is a textbook on modeling that happens to focus on financial instruments for the management of economic risk. The book combines a study of mathematical modeling with exposure to the tools of probability theory, difference and differential equations, numerical simulation, data analysis, and mathematical analysis. Students taking a course from Mathematical Modeling in Economics and Finance will come to understand some basic stochastic processes and the solutions to stochastic differential equations. They will understand how to use those tools to model the management of financial risk. They will gain a deep appreciation for the modeling process and learn methods of testing and evaluation driven by data. The reader of this book will be successfully positioned for an entry-level position in the financial services industry or for beginning graduate study in finance, economics, or actuarial science. The exposition in Mathematical Modeling in Economics and Finance is crystal clear and very student-friendly. The many exercises are extremely well designed. Steven Dunbar is Professor Emeritus of Mathematics at the University of Nebraska and he has won both university-wide and MAA prizes for extraordinary teaching. Dunbar served as Director of the MAA's American Mathematics Competitions from 2004 until 2015. His ability to communicate mathematics is on full display in this approachable, innovative text. |
| a first course in mathematical modeling: Methods of Mathematical Modelling Thomas Witelski, Mark Bowen, 2015-09-18 This book presents mathematical modelling and the integrated process of formulating sets of equations to describe real-world problems. It describes methods for obtaining solutions of challenging differential equations stemming from problems in areas such as chemical reactions, population dynamics, mechanical systems, and fluid mechanics. Chapters 1 to 4 cover essential topics in ordinary differential equations, transport equations and the calculus of variations that are important for formulating models. Chapters 5 to 11 then develop more advanced techniques including similarity solutions, matched asymptotic expansions, multiple scale analysis, long-wave models, and fast/slow dynamical systems. Methods of Mathematical Modelling will be useful for advanced undergraduate or beginning graduate students in applied mathematics, engineering and other applied sciences. |
| a first course in mathematical modeling: Principles of Mathematical Modeling Clive Dym, 2004-08-10 Science and engineering students depend heavily on concepts of mathematical modeling. In an age where almost everything is done on a computer, author Clive Dym believes that students need to understand and own the underlying mathematics that computers are doing on their behalf. His goal for Principles of Mathematical Modeling, Second Edition, is to engage the student reader in developing a foundational understanding of the subject that will serve them well into their careers. The first half of the book begins with a clearly defined set of modeling principles, and then introduces a set of foundational tools including dimensional analysis, scaling techniques, and approximation and validation techniques. The second half demonstrates the latest applications for these tools to a broad variety of subjects, including exponential growth and decay in fields ranging from biology to economics, traffic flow, free and forced vibration of mechanical and other systems, and optimization problems in biology, structures, and social decision making. Prospective students should have already completed courses in elementary algebra, trigonometry, and first-year calculus and have some familiarity with differential equations and basic physics. - Serves as an introductory text on the development and application of mathematical models - Focuses on techniques of particular interest to engineers, scientists, and others who model continuous systems - Offers more than 360 problems, providing ample opportunities for practice - Covers a wide range of interdisciplinary topics--from engineering to economics to the sciences - Uses straightforward language and explanations that make modeling easy to understand and apply New to this Edition: - A more systematic approach to mathematical modeling, outlining ten specific principles - Expanded and reorganized chapters that flow in an increasing level of complexity - Several new problems and updated applications - Expanded figure captions that provide more information - Improved accessibility and flexibility for teaching |
| a first course in mathematical modeling: Mathematical Modeling And Computation In Finance: With Exercises And Python And Matlab Computer Codes Cornelis W Oosterlee, Lech A Grzelak, 2019-10-29 This book discusses the interplay of stochastics (applied probability theory) and numerical analysis in the field of quantitative finance. The stochastic models, numerical valuation techniques, computational aspects, financial products, and risk management applications presented will enable readers to progress in the challenging field of computational finance.When the behavior of financial market participants changes, the corresponding stochastic mathematical models describing the prices may also change. Financial regulation may play a role in such changes too. The book thus presents several models for stock prices, interest rates as well as foreign-exchange rates, with increasing complexity across the chapters. As is said in the industry, 'do not fall in love with your favorite model.' The book covers equity models before moving to short-rate and other interest rate models. We cast these models for interest rate into the Heath-Jarrow-Morton framework, show relations between the different models, and explain a few interest rate products and their pricing.The chapters are accompanied by exercises. Students can access solutions to selected exercises, while complete solutions are made available to instructors. The MATLAB and Python computer codes used for most tables and figures in the book are made available for both print and e-book users. This book will be useful for people working in the financial industry, for those aiming to work there one day, and for anyone interested in quantitative finance. The topics that are discussed are relevant for MSc and PhD students, academic researchers, and for quants in the financial industry. |
| a first course in mathematical modeling: A First Course in Numerical Methods Uri M. Ascher, Chen Greif, 2011-07-14 Offers students a practical knowledge of modern techniques in scientific computing. |
| a first course in mathematical modeling: A First Course in Differential Equations J. David Logan, 2006-05-20 Therearemanyexcellenttextsonelementarydi?erentialequationsdesignedfor the standard sophomore course. However, in spite of the fact that most courses are one semester in length, the texts have evolved into calculus-like pres- tations that include a large collection of methods and applications, packaged with student manuals, and Web-based notes, projects, and supplements. All of this comes in several hundred pages of text with busy formats. Most students do not have the time or desire to read voluminous texts and explore internet supplements. The format of this di?erential equations book is di?erent; it is a one-semester, brief treatment of the basic ideas, models, and solution methods. Itslimitedcoverageplacesitsomewherebetweenanoutlineandadetailedte- book. I have tried to write concisely, to the point, and in plain language. Many worked examples and exercises are included. A student who works through this primer will have the tools to go to the next level in applying di?erential eq- tions to problems in engineering, science, and applied mathematics. It can give some instructors, who want more concise coverage, an alternative to existing texts. |
| a first course in mathematical modeling: Mathematical Methods and Models for Economists Angel de la Fuente, 2000-01-28 A textbook for a first-year PhD course in mathematics for economists and a reference for graduate students in economics. |
| a first course in mathematical modeling: Advanced Mathematical Modeling with Technology William P. Fox, Robert E. Burks, 2021-05-19 Mathematical modeling is both a skill and an art and must be practiced in order to maintain and enhance the ability to use those skills. Though the topics covered in this book are the typical topics of most mathematical modeling courses, this book is best used for individuals or groups who have already taken an introductory mathematical modeling course. This book will be of interest to instructors and students offering courses focused on discrete modeling or modeling for decision making. |
| a first course in mathematical modeling: Mathematical Modeling Stefan Heinz, 2011-07-03 The whole picture of Mathematical Modeling is systematically and thoroughly explained in this text for undergraduate and graduate students of mathematics, engineering, economics, finance, biology, chemistry, and physics. This textbook gives an overview of the spectrum of modeling techniques, deterministic and stochastic methods, and first-principle and empirical solutions. Complete range: The text continuously covers the complete range of basic modeling techniques: it provides a consistent transition from simple algebraic analysis methods to simulation methods used for research. Such an overview of the spectrum of modeling techniques is very helpful for the understanding of how a research problem considered can be appropriately addressed. Complete methods: Real-world processes always involve uncertainty, and the consideration of randomness is often relevant. Many students know deterministic methods, but they do hardly have access to stochastic methods, which are described in advanced textbooks on probability theory. The book develops consistently both deterministic and stochastic methods. In particular, it shows how deterministic methods are generalized by stochastic methods. Complete solutions: A variety of empirical approximations is often available for the modeling of processes. The question of which assumption is valid under certain conditions is clearly relevant. The book provides a bridge between empirical modeling and first-principle methods: it explains how the principles of modeling can be used to explain the validity of empirical assumptions. The basic features of micro-scale and macro-scale modeling are discussed – which is an important problem of current research. |
| a first course in mathematical modeling: Understanding Analysis Stephen Abbott, 2012-12-06 This elementary presentation exposes readers to both the process of rigor and the rewards inherent in taking an axiomatic approach to the study of functions of a real variable. The aim is to challenge and improve mathematical intuition rather than to verify it. The philosophy of this book is to focus attention on questions which give analysis its inherent fascination. Each chapter begins with the discussion of some motivating examples and concludes with a series of questions. |
| a first course in mathematical modeling: Practical Course In Differential Equations And Mathematical Modelling, A: Classical And New Methods. Nonlinear Mathematical Models. Symmetry And Invariance Principles Nail H Ibragimov, 2009-11-19 A Practical Course in Differential Equations and Mathematical Modelling is a unique blend of the traditional methods of ordinary and partial differential equations with Lie group analysis enriched by the author's own theoretical developments. The book — which aims to present new mathematical curricula based on symmetry and invariance principles — is tailored to develop analytic skills and “working knowledge” in both classical and Lie's methods for solving linear and nonlinear equations. This approach helps to make courses in differential equations, mathematical modelling, distributions and fundamental solution, etc. easy to follow and interesting for students. The book is based on the author's extensive teaching experience at Novosibirsk and Moscow universities in Russia, Collège de France, Georgia Tech and Stanford University in the United States, universities in South Africa, Cyprus, Turkey, and Blekinge Institute of Technology (BTH) in Sweden. The new curriculum prepares students for solving modern nonlinear problems and will essentially be more appealing to students compared to the traditional way of teaching mathematics. |
| a first course in mathematical modeling: Mathematical Modeling of Unsteady Inviscid Flows Jeff D. Eldredge, 2019-07-22 This book builds inviscid flow analysis from an undergraduate-level treatment of potential flow to the level required for research. The tools covered in this book allow the reader to develop physics-based mathematical models for a variety of flows, including attached and separated flows past wings, fins, and blades of various shapes undergoing arbitrary motions. The book covers all of the ingredients of these models: the solution of potential flows about arbitrary body shapes in two- and three-dimensional contexts, with a particular focus on conformal mapping in the plane; the decomposition of the flow into contributions from ambient vorticity and body motion; generalized edge conditions, of which the Kutta condition is a special case; and the calculation of force and moment, with extensive treatments of added mass and the influence of fluid vorticity. The book also contains an extensive primer with all of the necessary mathematical tools. The concepts are demonstrated on several example problems, both classical and modern. |
| a first course in mathematical modeling: Mathematical Modeling in Economics, Ecology and the Environment N.V. Hritonenko, Yuri P. Yatsenko, 2013-04-17 The problems of interrelation between human economics and natural environment include scientific, technical, economic, demographic, social, political and other aspects that are studied by scientists of many specialities. One of the important aspects in scientific study of environmental and ecological problems is the development of mathematical and computer tools for rational management of economics and environment. This book introduces a wide range of mathematical models in economics, ecology and environmental sciences to a general mathematical audience with no in-depth experience in this specific area. Areas covered are: controlled economic growth and technological development, world dynamics, environmental impact, resource extraction, air and water pollution propagation, ecological population dynamics and exploitation. A variety of known models are considered, from classical ones (Cobb Douglass production function, Leontief input-output analysis, Solow models of economic dynamics, Verhulst-Pearl and Lotka-Volterra models of population dynamics, and others) to the models of world dynamics and the models of water contamination propagation used after Chemobyl nuclear catastrophe. Special attention is given to modelling of hierarchical regional economic-ecological interaction and technological change in the context of environmental impact. Xlll XIV Construction of Mathematical Models ... |
| a first course in mathematical modeling: A Course in Mathematical Modeling Douglas D. Mooney, Randall J. Swift, 2021-11-15 The emphasis of this book lies in the teaching of mathematical modeling rather than simply presenting models. To this end the book starts with the simple discrete exponential growth model as a building block, and successively refines it. This involves adding variable growth rates, multiple variables, fitting growth rates to data, including random elements, testing exactness of fit, using computer simulations and moving to a continuous setting. No advanced knowledge is assumed of the reader, making this book suitable for elementary modeling courses. The book can also be used to supplement courses in linear algebra, differential equations, probability theory and statistics. |
| a first course in mathematical modeling: Exploring Mathematical Modeling in Biology Through Case Studies and Experimental Activities Rebecca Sanft, Anne Walter, 2020-04-01 Exploring Mathematical Modeling in Biology through Case Studies and Experimental Activities provides supporting materials for courses taken by students majoring in mathematics, computer science or in the life sciences. The book's cases and lab exercises focus on hypothesis testing and model development in the context of real data. The supporting mathematical, coding and biological background permit readers to explore a problem, understand assumptions, and the meaning of their results. The experiential components provide hands-on learning both in the lab and on the computer. As a beginning text in modeling, readers will learn to value the approach and apply competencies in other settings. Included case studies focus on building a model to solve a particular biological problem from concept and translation into a mathematical form, to validating the parameters, testing the quality of the model and finally interpreting the outcome in biological terms. The book also shows how particular mathematical approaches are adapted to a variety of problems at multiple biological scales. Finally, the labs bring the biological problems and the practical issues of collecting data to actually test the model and/or adapting the mathematics to the data that can be collected. |
| a first course in mathematical modeling: A First Course in Structural Equation Modeling Tenko Raykov, George A. Marcoulides, 2012-08-21 In this book, authors Tenko Raykov and George A. Marcoulides introduce students to the basics of structural equation modeling (SEM) through a conceptual, nonmathematical approach. For ease of understanding, the few mathematical formulas presented are used in a conceptual or illustrative nature, rather than a computational one. Featuring examples from EQS, LISREL, and Mplus, A First Course in Structural Equation Modeling is an excellent beginner’s guide to learning how to set up input files to fit the most commonly used types of structural equation models with these programs. The basic ideas and methods for conducting SEM are independent of any particular software. Highlights of the Second Edition include: • Review of latent change (growth) analysis models at an introductory level • Coverage of the popular Mplus program • Updated examples of LISREL and EQS • Downloadable resources that contains all of the text’s LISREL, EQS, and Mplus examples. A First Course in Structural Equation Modeling is intended as an introductory book for students and researchers in psychology, education, business, medicine, and other applied social, behavioral, and health sciences with limited or no previous exposure to SEM. A prerequisite of basic statistics through regression analysis is recommended. The book frequently draws parallels between SEM and regression, making this prior knowledge helpful. |
| a first course in mathematical modeling: Mathematical Modeling Jonas Hall, Thomas Lingefjärd, 2016-06-13 A logical problem-based introduction to the use of GeoGebra for mathematical modeling and problem solving within various areas of mathematics A well-organized guide to mathematical modeling techniques for evaluating and solving problems in the diverse field of mathematics, Mathematical Modeling: Applications with GeoGebra presents a unique approach to software applications in GeoGebra and WolframAlpha. The software is well suited for modeling problems in numerous areas of mathematics including algebra, symbolic algebra, dynamic geometry, three-dimensional geometry, and statistics. Featuring detailed information on how GeoGebra can be used as a guide to mathematical modeling, the book provides comprehensive modeling examples that correspond to different levels of mathematical experience, from simple linear relations to differential equations. Each chapter builds on the previous chapter with practical examples in order to illustrate the mathematical modeling skills necessary for problem solving. Addressing methods for evaluating models including relative error, correlation, square sum of errors, regression, and confidence interval, Mathematical Modeling: Applications with GeoGebra also includes: Over 400 diagrams and 300 GeoGebra examples with practical approaches to mathematical modeling that help the reader develop a full understanding of the content Numerous real-world exercises with solutions to help readers learn mathematical modeling techniques A companion website with GeoGebra constructions and screencasts Mathematical Modeling: Applications with GeoGebrais ideal for upper-undergraduate and graduate-level courses in mathematical modeling, applied mathematics, modeling and simulation, operations research, and optimization. The book is also an excellent reference for undergraduate and high school instructors in mathematics. |
| a first course in mathematical modeling: Mathematical Modeling with Excel Brian Albright, William P Fox, 2019-11-25 This text presents a wide variety of common types of models found in other mathematical modeling texts, as well as some new types. However, the models are presented in a very unique format. A typical section begins with a general description of the scenario being modeled. The model is then built using the appropriate mathematical tools. Then it is implemented and analyzed in Excel via step-by-step instructions. In the exercises, we ask students to modify or refine the existing model, analyze it further, or adapt it to similar scenarios. |
Last name 和 First name 到底哪个是名哪个是姓？ - 知乎
Last name 和 First name 到底哪个是名哪个是姓？ 上学的时候老师说因为英语文化中名在前，姓在后，所以Last name是姓，first name是名，假设一个中国人叫孙悟空，那么他的first nam…
first 和 firstly 的用法区别是什么？ - 知乎
a.First （ = First of all）I must finish this work.（含义即，先完成这项工作再说，因为这是必须的，重要的，至于其它，再说吧） b.First come，first served .先来，先招待（最重要） …
EndNote如何设置参考文献英文作者姓全称，名缩写？ - 知乎
知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业 …
对一个陌生的英文名字，如何快速确定哪个是姓哪个是名？ - 知乎
知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业 …
发表sci共同第一作者(排名第二)有用吗？ - 知乎
知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业 …
有大神公布一下Nature Communications从投出去到Online的审稿 …
知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业 …
贝塞尔函数及其性质 - 知乎
为第一类贝塞尔函数 (Bessel functions of the first kind)， 为第二类贝塞尔函数 (Bessel functions of the second kind)，有的也记为 。 第一类贝塞尔函数积分表达式. 对于整数阶n， 该公式也 …
论文作者后标注了共同一作（数字1）但没有解释标注还算共一 …
Aug 26, 2022 · 是在不同作者姓名的右上角标了数字1吗？ 共同作者可不是这么标的。 标注共同一作的方法并不是有的作者以为的上下并列，而是在共同第一作者的右上角标注相同的符号，比 …
什么是第一性原理，它有什么重要意义？ - 知乎
02 如何理解第一性原理？. 两千多年前， 亚里士多德这样表述第一性原理： 在每一系统的探索中，存在第一性原理（First principle thinking），它是一个最基本的命题或假设，不能被省略或 …
2025年618 CPU选购指南丨CPU性能天梯图（R23 单核/多核性能跑 …
May 4, 2025 · cpu型号名称小知识 amd. 无后缀 ：普通型号; 后缀 g ：有高性能核显型号（5000系及之前系列 除了后缀有g的其他均为 无核显，7000除了后缀f，都有核显）
Last name 和 First name 到底哪个是名哪个是姓？ - 知乎
Last name 和 First name 到底哪个是名哪个是姓？ 上学的时候老师说因为英语文化中名在前，姓在后，所以Last name是姓，first name是名，假设一个中国人叫孙悟空，那么他的first nam…
first 和 firstly 的用法区别是什么？ - 知乎
a.First （ = First of all）I must finish this work.（含义即，先完成这项工作再说，因为这是必须的，重要的，至于其它，再说吧） b.First come，first served .先来，先招待（最重要） …
EndNote如何设置参考文献英文作者姓全称，名缩写？ - 知乎
知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业 …
对一个陌生的英文名字，如何快速确定哪个是姓哪个是名？ - 知乎
知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业 …
发表sci共同第一作者(排名第二)有用吗？ - 知乎
知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业 …
有大神公布一下Nature Communications从投出去到Online的审稿 …
知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业 …
贝塞尔函数及其性质 - 知乎
为第一类贝塞尔函数 (Bessel functions of the first kind)， 为第二类贝塞尔函数 (Bessel functions of the second kind)，有的也记为 。 第一类贝塞尔函数积分表达式. 对于整数阶n， 该公式也 …
论文作者后标注了共同一作（数字1）但没有解释标注还算共一 …
Aug 26, 2022 · 是在不同作者姓名的右上角标了数字1吗？ 共同作者可不是这么标的。 标注共同一作的方法并不是有的作者以为的上下并列，而是在共同第一作者的右上角标注相同的符号，比 …
什么是第一性原理，它有什么重要意义？ - 知乎
02 如何理解第一性原理？. 两千多年前， 亚里士多德这样表述第一性原理： 在每一系统的探索中，存在第一性原理（First principle thinking），它是一个最基本的命题或假设，不能被省略或 …
2025年618 CPU选购指南丨CPU性能天梯图（R23 单核/多核性能 …
May 4, 2025 · cpu型号名称小知识 amd. 无后缀 ：普通型号; 后缀 g ：有高性能核显型号（5000系及之前系列 除了后缀有g的其他均为 无核显，7000除了后缀f，都有核显）
