Mathematical Model of Gravitational and Electrostatic Forces

Total Page:16

File Type:pdf, Size:1020Kb

Mathematical Model of Gravitational and Electrostatic Forces Mathematical Model of Gravitational and Electrostatic Forces Alexei Krouglov Email: [email protected] ABSTRACT Author presents mathematical model for acting-on-a-distance attractive and repulsive forces based on propagation of energy waves that produces Newton expression for gravitational and Coulomb expression for electrostatic forces. Model uses mathematical observation that difference between two inverse exponential functions of the distance asymptotically converges to function proportional to reciprocal of distance squared. Keywords: Gravitational Force; Electrostatic Force; Field Theory 2 1. Introduction Author described his Dual Time-Space Model of Wave Propagation in work [2]. He tries to express physical phenomena based only on concept of energy. Model asserts that when at some point in space and in time local energy value is different from global energy level in surrounding area, energy disturbance is created at this point. Energy disturbance propagates both in time and in space as energy wave, and oscillates. Author used the model to describe both propagation of energy waves in physics [3] – [5] and fluctuations of demand and supply in economics [6], [7]. Author presented in [3] mathematical model for attractive and repulsive forces that demonstrated how gravitational and electrostatic forces are created on a distance based on instrument of Dual Time-Space Model of Wave Propagation. However paper [3] didn’t explain one important point. It described forces acting on a distance as being proportional to inverse exponential function of distance. Lately author realized that difference between two inverse exponential functions of distance is asymptotically converging to function that is proportional to reciprocal of distance squared. Thus result provided by the model is in agreement with experimentally obtained formulas for forces acting on a distance such as gravitational and electrostatic forces. In current paper author presents quantitative mathematical model for acting-on-a- distance static forces. 3 2. Model Methodology As it was described in [2], each point in space and in time is characterized by two energy values – local energy value in point U (x,t) and global energy level in neighboring area Φ(x,t). The difference between quantities U (x,t) and Φ(x,t) creates energy disturbance, which propagates both in time and in space according to ordinary differential equations presented below. Differential equations in time domain are, dP()x ,t 0 = −λ (U ()x ,t − Φ(x ,t)) (1) dt t 0 0 d 2U ()x ,t dP()x ,t 0 = µ 0 (2) dt 2 t dt dΦ()x ,t dP()x ,t 0 = −ν 0 (3) dt t dt where x0 ∈ ℜ , λt , µt ,ν t ≥ 0 are constants, and P(x0 ,t) is dual variable evaluated in point x0 at time t . Briefly speaking, meaning of equations (1) – (3) is as follows. Second derivative of energy value U (x,t) with respect to time is inversely proportional to energy disturbance U ()x,t − Φ(x,t) . First derivative of energy level Φ(x,t) with respect to time is directly proportional to energy disturbance U ()x,t − Φ(x,t) . We can rewrite equations (1) – (3) to describe dynamics of changes for energy disturbance E()x0 ,t = U (x0 ,t)− Φ(x0 ,t) in time, d 2 E()x ,t dE()x ,t 0 +ν λ 0 + µ λ E()x ,t = 0 (4) dt 2 t t dt t t 0 4 Equation (4) is an ordinary differential equation of the second order, and can be resolved by regular methods (for example, see [1]). Note that value E()x0 ,t → 0 when t → +∞ for all x0 . Differential equations in space domain are, dP()x,t 0 = −λ (U ()x,t − Φ(x,t )) (5) dx x 0 0 d 2U ()x,t dP()x,t 0 = µ 0 (6) dx 2 x dx dΦ()x,t dP()x,t 0 = −ν 0 (7) dx x dx + where t0 ∈ℜ , λx , µ x ,ν x ≥ 0 are constants. Similarly as above, meaning of equations (5) – (7) is as follows. Second derivative of energy value U (x,t) with respect to the distance in space is inversely proportional to energy disturbance E(x,t). First derivative of energy level Φ(x,t) with respect to the distance in space is directly proportional to energy disturbance E()x,.t We can rewrite equations (5) – (7) to describe dynamics of changes for energy disturbance E(x,t0 ) in space, d 2 E()x,t dE()x,t 0 +ν λ 0 + µ λ E()x,t = 0 (8) dx 2 x x dx x x 0 Equation (8) is also an ordinary differential equation of the second order. Here again value E()x,t0 → 0 when x → +∞ for all t0 > 0 . One of primary characteristics of current model is that it uses three variables to describe dynamics of wave propagation. Nonetheless present paper is devoted to static 5 phenomena, particularly to formation of static forces acting on a distance between solid bodies. Dynamics of wave propagation in space will involve a special study. 3. Transformation of Global Energy Level near Solid Body Author considers in current paper the static situation t ≡ t0 . It means that energy disturbance is not propagated in time. Here we deal with solid bodies – it means from the model’s point of view that energy disturbance is not propagated in space with regard to local energy values U (x,t). Thus we only consider transformation of global energy level Φ()x,t . Mathematically speaking, static situation means that equations (1) – (3) have null coefficients λt , µt ,ν t = 0 . Solid body means null coefficient µ x = 0 in equations (5) – (7) (other coefficients in equations (5) – (7) are nonnegative λx ,ν x ≥ 0 ). Therefore equations (5) and (7) can be combined into the following one, dΦ()x,t = −ν λ (Φ()x,t −U (x,t)) (9) dx x x Since we are dealing with solid body, local energy value U (x,t) can’t propagate behind body’s boundaries x0 ∈ ℜ . The situation is illustrated on Figure 1, which shows transformation of energy level in space near boundary of solid body. Figure 1a shows situation when no energy disturbance is present and local energy values in space match global energy level in surrounding area. Figure 1b displays situation on the boundary of solid body where local energy value abruptly changes its 6 magnitude. That change creates energy disturbance, which transforms global energy level behind the boundaries of solid body. To calculate transformation of global energy level one has to solve equation (9) for x1 > x0 . We have following initial conditions at the body’s boundary x0 ∈ ℜ for ∀t > 0 , U ()x0 ,t ≡ Φ()x0 ,t ≡ U 0 . We assume that U ()x1 ,t ≡ U1 is constant for x1 > x0 and ∀t > 0 , and equation (9) for global energy level Φ(x,t) can produce following equation for energy disturbance E()x,t = U1 − Φ()x,t , dE()x,t = −ν λ E(x,t) (10) dx x x where x > x0 , ∀t > 0 , and E()x0 ,.t = U (x1 ,t)()− Φ x0 ,t = U1 −U 0 = −∆U Thus solution of equation (10) is as follows for x > x0 , E()x,t = E0 exp{−ν x λx (x − x0 )} (11) where E0 = −∆U . Therefore solution of equation (9) is for x > x0 , Φ()x,t = (U 0 −U1 )exp{−ν xλx (x − x0 )}+U1 (12) since ∆U = −E0 = U 0 −U1 . Figure 1c illustrates how global energy level is transformed near the boundary of solid body according to equation (12). 7 4. Forces near Solid Body at Rest As we know, concept of physical forces is related with ideas of work and distance, and particularly – multiplication of average force applied to body by distance equals to the work, where work equals to change of body’s energy. Following to discussion above, when a solid body has energy disturbance, it creates energy disbalance in surrounding area. If energy disbalance occurs on the boundary of solid body, the body is trying to move to another position in order to reduce this energy disbalance. Thus energy disturbance creates physical force. The magnitude of that force is proportional to changes of absolute value of energy disturbance with respect to distance in space. Force points to direction of decrease of absolute value of energy disturbance. Next definition of force is used in the model. Definition. Instantaneous change of absolute value of energy disturbance at the d E()x ,t boundary of solid body 0 in point x with respect to distance in space is called dx 0 force F(x0 ,t) applied to solid body in point x0 and directed to the decrease of absolute value of energy disturbance, d E()x ,t d U ()()x ,t − Φ x ,t F()x ,t ≡ −k 0 = −k 0 0 (13) 0 dx dx where k > 0 is coefficient of proportionality. Let’s look how this definition of force works for solid body that is placed at rest. The situation with solid body at rest is illustrated on Figure 2. Figure 2a displays local energy values U (x,t) and global energy level Φ()x,t near solid body. Figure 2b presents distribution of energy disturbance E(x,t) near solid 8 body. Figure 2c examines adjusted distribution of energy disturbance E(x,t) near solid body, which takes into account transformation of global energy level on each side of the solid body’s border (this correction of global energy level is not further used in the paper). Thus if points in space x0 ∈ ℜ and x2 ∈ ℜ , where x0 > x2 , are respectively right and left boundaries of solid body, transformation of global energy level Φ(x,t) behind right and left boundaries of solid body is described as, ⎧∆U exp{}−ν x λx ()x − x0 +U1 if x > x0 Φ()x,t = ⎨ (14) ⎩ ∆U exp{}ν x λx ()x − x2 +U1 if x < x2 Then forces F(x0 ,t) and F(x2 ,t) acting on solid body at its boundaries are as follows (function E()x,t = U1 − Φ(x,t) is decreasing to the right of body’s boundary x > x0 , and is increasing to the left of body’s boundary x < x2 ), E()x,t − E()x0 ,t Φ()x,t − Φ(x0 ,t) dΦ()x0 ,t F()x0 ,t = − lim k = − lim k = −k =ν x λx k ∆U x→x x→x 0 x − x0 0 x − x0 dx x>x0 x>x0 (15) E()x,t − E()x2 ,t Φ()x,t − Φ(x2 ,t) dΦ()x2 ,t F()x2 ,t = − lim k = − lim k = −k = −ν x λx k ∆U x→x x→x 2 x − x2 2 x − x2 dx x<x2 x<x2 (16) Since we assumed above that solid body is at rest (i.e.
Recommended publications
  • 2.3: Modeling with Differential Equations Some General Comments: a Mathematical Model Is an Equation Or Set of Equations That Mi
    2.3: Modeling with Differential Equations Some General Comments: A mathematical model is an equation or set of equations that mimic the behavior of some phenomenon under certain assumptions/approximations. Phenomena that contain rates/change can often be modeled with differential equations. Disclaimer: In forming a mathematical model, we make various assumptions and simplifications. I am never going to claim that these models perfectly fit physical reality. But mathematical modeling is a key component of the following scientific method: 1. We make assumptions (a hypothesis) and form a model. 2. We mathematically analyze the model. (For differential equations, these are the techniques we are learning this quarter). 3. If the model fits the phenomena well, then we have evidence that the assumptions of the model might be valid. If the model fits the phenomena poorly, then we learn that some of our assumptions are invalid (so we still learn something). Concerning this course: I am not expecting you to be an expect in forming mathematical models. For this course and on exams, I expect that: 1. You understand how to do all the homework! If I give you a problem on the exam that is very similar to a homework problem, you must be prepared to show your understanding. 2. Be able to translate a basic statement involving rates and language like in the homework. See my previous posting on applications for practice (and see homework from 1.1, 2.3, 2.5 and throughout the other assignments). 3. If I give an application on an exam that is completely new and involves language unlike homework, then I will give you the differential equation (you won’t have to make up a new model).
    [Show full text]
  • An Introduction to Mathematical Modelling
    An Introduction to Mathematical Modelling Glenn Marion, Bioinformatics and Statistics Scotland Given 2008 by Daniel Lawson and Glenn Marion 2008 Contents 1 Introduction 1 1.1 Whatismathematicalmodelling?. .......... 1 1.2 Whatobjectivescanmodellingachieve? . ............ 1 1.3 Classificationsofmodels . ......... 1 1.4 Stagesofmodelling............................... ....... 2 2 Building models 4 2.1 Gettingstarted .................................. ...... 4 2.2 Systemsanalysis ................................. ...... 4 2.2.1 Makingassumptions ............................. .... 4 2.2.2 Flowdiagrams .................................. 6 2.3 Choosingmathematicalequations. ........... 7 2.3.1 Equationsfromtheliterature . ........ 7 2.3.2 Analogiesfromphysics. ...... 8 2.3.3 Dataexploration ............................... .... 8 2.4 Solvingequations................................ ....... 9 2.4.1 Analytically.................................. .... 9 2.4.2 Numerically................................... 10 3 Studying models 12 3.1 Dimensionlessform............................... ....... 12 3.2 Asymptoticbehaviour ............................. ....... 12 3.3 Sensitivityanalysis . ......... 14 3.4 Modellingmodeloutput . ....... 16 4 Testing models 18 4.1 Testingtheassumptions . ........ 18 4.2 Modelstructure.................................. ...... 18 i 4.3 Predictionofpreviouslyunuseddata . ............ 18 4.3.1 Reasonsforpredictionerrors . ........ 20 4.4 Estimatingmodelparameters . ......... 20 4.5 Comparingtwomodelsforthesamesystem . .........
    [Show full text]
  • Lecture Notes1 Mathematical Ecnomics
    Lecture Notes1 Mathematical Ecnomics Guoqiang TIAN Department of Economics Texas A&M University College Station, Texas 77843 ([email protected]) This version: August, 2020 1The most materials of this lecture notes are drawn from Chiang’s classic textbook Fundamental Methods of Mathematical Economics, which are used for my teaching and con- venience of my students in class. Please not distribute it to any others. Contents 1 The Nature of Mathematical Economics 1 1.1 Economics and Mathematical Economics . 1 1.2 Advantages of Mathematical Approach . 3 2 Economic Models 5 2.1 Ingredients of a Mathematical Model . 5 2.2 The Real-Number System . 5 2.3 The Concept of Sets . 6 2.4 Relations and Functions . 9 2.5 Types of Function . 11 2.6 Functions of Two or More Independent Variables . 12 2.7 Levels of Generality . 13 3 Equilibrium Analysis in Economics 15 3.1 The Meaning of Equilibrium . 15 3.2 Partial Market Equilibrium - A Linear Model . 16 3.3 Partial Market Equilibrium - A Nonlinear Model . 18 3.4 General Market Equilibrium . 19 3.5 Equilibrium in National-Income Analysis . 23 4 Linear Models and Matrix Algebra 25 4.1 Matrix and Vectors . 26 i ii CONTENTS 4.2 Matrix Operations . 29 4.3 Linear Dependance of Vectors . 32 4.4 Commutative, Associative, and Distributive Laws . 33 4.5 Identity Matrices and Null Matrices . 34 4.6 Transposes and Inverses . 36 5 Linear Models and Matrix Algebra (Continued) 41 5.1 Conditions for Nonsingularity of a Matrix . 41 5.2 Test of Nonsingularity by Use of Determinant .
    [Show full text]
  • Limitations on the Use of Mathematical Models in Transportation Policy Analysis
    Limitations on the Use of Mathematical Models in Transportation Policy Analysis ABSTRACT: Government agencies are using many kinds of mathematical models to forecast the effects of proposed government policies. Some models are useful; others are not; all have limitations. While modeling can contribute to effective policyma king, it can con tribute to poor decision-ma king if policymakers cannot assess the quality of a given application. This paper describes models designed for use in policy analyses relating to the automotive transportation system, discusses limitations of these models, and poses questions policymakers should ask about a model to be sure its use is appropriate. Introduction Mathematical modeling of real-world use in analyzing the medium and long-range ef- systems has increased significantly in the past fects of federal policy decisions. A few of them two decades. Computerized simulations of have been applied in federal deliberations con- physical and socioeconomic systems have cerning policies relating to energy conserva- proliferated as federal agencies have funded tion, environmental pollution, automotive the development and use of such models. A safety, and other complex issues. The use of National Science Foundation study established mathematical models in policy analyses re- that between 1966 and 1973 federal agencies quires that policymakers obtain sufficient infor- other than the Department of Defense sup- mation on the models (e-g., their structure, ported or used more than 650 models limitations, relative reliability of output) to make developed at a cost estimated at $100 million informed judgments concerning the value of (Fromm, Hamilton, and Hamilton 1974). Many the forecasts the models produce.
    [Show full text]
  • Introduction to Mathematical Modeling Difference Equations, Differential Equations, & Linear Algebra (The First Course of a Two-Semester Sequence)
    Introduction to Mathematical Modeling Difference Equations, Differential Equations, & Linear Algebra (The First Course of a Two-Semester Sequence) Dr. Eric R. Sullivan [email protected] Department of Mathematics Carroll College, Helena, MT Content Last Updated: January 8, 2018 1 ©Eric Sullivan. Some Rights Reserved. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. You may copy, distribute, display, remix, rework, and per- form this copyrighted work, but only if you give credit to Eric Sullivan, and all deriva- tive works based upon it must be published under the Creative Commons Attribution- NonCommercial-Share Alike 4.0 United States License. Please attribute this work to Eric Sullivan, Mathematics Faculty at Carroll College, [email protected]. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-sa/4.0/ or send a letter to Creative Commons, 171 Second Street, Suite 300, San Francisco, Cali- fornia, 94105, USA. Contents 0 To the Student and the Instructor5 0.1 An Inquiry Based Approach...........................5 0.2 Online Texts and Other Resources........................7 0.3 To the Instructor..................................7 1 Fundamental Notions from Calculus9 1.1 Sections from Active Calculus..........................9 1.2 Modeling Explorations with Calculus...................... 10 2 An Introduction to Linear Algebra 16 2.1 Why Linear Algebra?............................... 16 2.2 Matrix Operations and Gaussian Elimination................. 20 2.3 Gaussian Elimination: A First Look At Solving Systems........... 23 2.4 Systems of Linear Equations........................... 28 2.5 Linear Combinations............................... 35 2.6 Inverses and Determinants............................ 38 2.6.1 Inverses................................... 40 2.6.2 Determinants..............................
    [Show full text]
  • What Is Game Theory Trying to Accomplish?
    1 What Is Game Theory Trying to Accomplish? 1 Introduction The language of game theory—coalitions, payo¤s, markets, votes— suggests that it is not a branch of abstract mathematics; that it is moti- vated by and related to the world around us; and that it should be able to tell us something about that world. Most of us have long realized that game theory and the ‘‘real world’’ (it might better be called the complex world) have a relationship that is not entirely comfortable; that it is not clear just what it is that we are trying to do when we build a game- theoretic model and then apply solution concepts to it. This is the subject I would like to explore in this paper. I might add that much the same questions apply to economic theory, at least the kind that those of us working in mathematical economics see most often; and that much of my paper will apply, mutatis mutandis, to economic theory as well. There is a branch of philosophy that deals with theory in the social sciences, so some of the things I have to say are unquestionably old hat. But I am not trying to be particularly original: I am only trying to open this topic, which I think concerns all of us, for discussion, and to suggest a particu- lar point of view. No doubt others have thought about these questions more thoroughly and deeply than I have, and are better versed in the history and philosophy of science in general. I will be grateful to anybody who sets me straight when I err, and who gives me references for the things I get right.
    [Show full text]
  • A Short Introduction to Game Theory
    A Short Introduction to Game Theory Heiko Hotz CONTENTS 1 Contents 1 Introduction 2 1.1 Game Theory – What is it? . 2 1.2 Game Theory – Where is it applied? . 2 2 Definitions 3 2.1 Normal Form Games . 3 2.2 Extensive Form Games . 3 2.3 Nash Equilibrium . 4 2.3.1 Best Response . 4 2.3.2 Localizing a Nash Equilibrium in a Payoff-matrix . 4 2.4 Mixed Strategies . 5 3 Games 5 3.1 Prisoner’s Dilemma (PD) . 5 3.1.1 Other Interesting Two-person Games . 6 3.2 The Ultimatum Game . 7 3.3 Public Good Game . 7 3.4 Rock, Paper, Scissors . 8 4 Evolutionary Game Theory 8 4.1 Why EGT? . 8 4.2 Evolutionary Stable Strategies . 9 4.2.1 ESS and Nash Equilibrium . 9 4.2.2 The Hawk-Dove Game . 9 4.2.3 ESS of the Hawk-Dove Game . 10 4.3 The Replicator Dynamics . 11 4.4 ESS and Replicator Dynamics . 11 5 Applications 12 5.1 Evolution of cooperation . 12 5.2 Biodiversity . 12 A Mathematical Derivation 14 A.1 Normal form games . 14 A.2 Nash equilibrium and best answer . 14 A.3 Evolutionary stable strategies (ESS) . 14 A.4 Replicator equation . 15 A.5 Evolutionary stable state of the Hawk-Dove game . 15 B Pogram Code 17 B.1 Spatial PD . 17 B.2 Hawk-Dove game . 19 1 INTRODUCTION 2 1 Introduction This description leads to a widespred definition of game theory: This paper gives a brief overview of game theory. Therefore in the first section I The subject of game theory want to outline what game theory gener- are situations, where the re- ally is and where it is applied.
    [Show full text]
  • What Is Mathematical Modeling?
    1 What Is Mathematical Modeling? We begin this book with a dictionary definition of the word model: model (n): a miniature representation of something; a pattern of some- thing to be made; an example for imitation or emulation; a description or analogy used to help visualize something (e.g., an atom) that cannot be dir- ectly observed; a system of postulates, data and inferences presented as a mathematical description of an entity or state of affairs This definition suggests that modeling is an activity, a cognitive activity in which we think about and make models to describe how devices or objects of interest behave. There are many ways in which devices and behaviors can be described. We can use words, drawings or sketches, physical models, computer pro- grams, or mathematical formulas. In other words, the modeling activity can be done in several languages, often simultaneously. Since we are par- ticularly interested in using the language of mathematics to make models, 3 4 Chapter 1 What Is Mathematical Modeling? we will refine the definition just given: mathematical model (n): a representation in mathematical terms of the behavior of real devices and objects We want to know how to make or generate mathematical representations or models, how to validate them, how to use them, and how and when their use is limited. But before delving into these important issues, it is worth talking about why we do mathematical modeling. 1.1 WhyDo We Do Mathematical Modeling? Since the modeling of devices and phenomena is essential to both engi- neering and science, engineers and scientists have very practical reasons for doing mathematical modeling.
    [Show full text]
  • Basic Concepts and Approaches*
    Educational Sciences: Theory & Practice • 14(4) • 1621-1627 ©2014 Educational Consultancy and Research Center www.edam.com.tr/estp DOI: 10.12738/estp.2014.4.2039 Mathematical Modeling in Mathematics Education: Basic Concepts and Approaches* Ayhan Kürşat ERBAŞa Mahmut KERTİLb Middle East Technical University Marmara University Bülent ÇETİNKAYAc Erdinç ÇAKIROĞLUd Middle East Technical University Middle East Technical University Cengiz ALACACIe Sinem BAŞf İstanbul Medeniyet University İstanbul Aydın University Abstract Mathematical modeling and its role in mathematics education have been receiving increasing attention in Turkey, as in many other countries. The growing body of literature on this topic reveals a variety of approaches to mathematical modeling and related concepts, along with differing perspectives on the use of mathematical modeling in teaching and learning mathematics in terms of definitions of models and modeling, the theoretical backgrounds of modeling, and the nature of questions used in teaching modeling. This study focuses on two issues. The first section attempts to develop a unified perspective about mathematical modeling. The second section analyzes and discusses two approaches to the use of modeling in mathematics education, namely modeling as a means of teaching mathematics and modeling as an aim of teaching mathematics. Keywords Mathematics Education, Mathematical Model, Mathematical Modeling, Problem Solving. * Work reported here is based on a research project supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under grant number 110K250. Opinions expressed are those of the authors and do not necessarily represent those of TUBITAK. Ayhan Kursat Erbas is supported by the Turkish Academy of Sciences through the Young Scientist Award Program (A.K.E./TÜBA-GEBİP/2012-11).
    [Show full text]
  • Introduction to Game Theory: a Discovery Approach
    Linfield University DigitalCommons@Linfield Linfield Authors Book Gallery Faculty Scholarship & Creative Works 2020 Introduction to Game Theory: A Discovery Approach Jennifer Firkins Nordstrom Linfield College Follow this and additional works at: https://digitalcommons.linfield.edu/linfauth Part of the Logic and Foundations Commons, Set Theory Commons, and the Statistics and Probability Commons Recommended Citation Nordstrom, Jennifer Firkins, "Introduction to Game Theory: A Discovery Approach" (2020). Linfield Authors Book Gallery. 83. https://digitalcommons.linfield.edu/linfauth/83 This Book is protected by copyright and/or related rights. It is brought to you for free via open access, courtesy of DigitalCommons@Linfield, with permission from the rights-holder(s). Your use of this Book must comply with the Terms of Use for material posted in DigitalCommons@Linfield, or with other stated terms (such as a Creative Commons license) indicated in the record and/or on the work itself. For more information, or if you have questions about permitted uses, please contact [email protected]. Introduction to Game Theory a Discovery Approach Jennifer Firkins Nordstrom Linfield College McMinnville, OR January 4, 2020 Version 4, January 2020 ©2020 Jennifer Firkins Nordstrom This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Preface Many colleges and universities are offering courses in quantitative reasoning for all students. One model for a quantitative reasoning course is to provide students with a single cohesive topic. Ideally, such a topic can pique the curios- ity of students with wide ranging academic interests and limited mathematical background. This text is intended for use in such a course.
    [Show full text]
  • THE DYNAMICAL SYSTEMS APPROACH to DIFFERENTIAL EQUATIONS INTRODUCTION the Mathematical Subject We Call Dynamical Systems Was
    BULLETIN (New Series) OF THE AMERICAN MATHEMATICAL SOCIETY Volume 11, Number 1, July 1984 THE DYNAMICAL SYSTEMS APPROACH TO DIFFERENTIAL EQUATIONS BY MORRIS W. HIRSCH1 This harmony that human intelligence believes it discovers in nature —does it exist apart from that intelligence? No, without doubt, a reality completely independent of the spirit which conceives it, sees it or feels it, is an impossibility. A world so exterior as that, even if it existed, would be forever inaccessible to us. But what we call objective reality is, in the last analysis, that which is common to several thinking beings, and could be common to all; this common part, we will see, can be nothing but the harmony expressed by mathematical laws. H. Poincaré, La valeur de la science, p. 9 ... ignorance of the roots of the subject has its price—no one denies that modern formulations are clear, elegant and precise; it's just that it's impossible to comprehend how any one ever thought of them. M. Spivak, A comprehensive introduction to differential geometry INTRODUCTION The mathematical subject we call dynamical systems was fathered by Poin­ caré, developed sturdily under Birkhoff, and has enjoyed a vigorous new growth for the last twenty years. As I try to show in Chapter I, it is interesting to look at this mathematical development as the natural outcome of a much broader theme which is as old as science itself: the universe is a sytem which changes in time. Every mathematician has a particular way of thinking about mathematics, but rarely makes it explicit.
    [Show full text]
  • Mathematical Modelling in Physics and Engineering
    MATHEMATICAL MODELLING IN PHYSICS AND ENGINEERING Wydawnictwo Wydziału Zarządzania Politechniki Częstochowskiej Częstochowa 2016 Mathematical Modelling in Physics and Engineering Scientific editors: Andrzej Grzybowski Stanisław Kukla Technical editors: Urszula Siedlecka Izabela Zamorska Cover design: Tomasz Błaszczyk Jock Cooper, www.fractal-recurrious.com Scientific committee: Organizing committee: Tomasz Błaszczyk CUT Marek Błasik Mariusz Ciesielski CUT Tomasz Błaszczyk Zbigniew Domański CUT Mariusz Ciesielski Andrzej Drzewiński UZG Andrzej Grzybowski Andrzej Grzybowski CUT Jolanta Pozorska Małgorzata Klimek CUT Urszula Siedlecka Bohdan Kopytko CUT Izabela Zamorska Stanisław Kukla CUT Jacek Leszczyński AGH UST Valerie Novitzka TUK Antoni Pierzchalski UL Zbigniew Pozorski PUT Piotr Puchała CUT Grażyna Rygał JDU Norbert Sczygiol CUT Based on the materials submitted by the authors ISBN 978-83-65179-49-4 © Copyright by Wydawnictwo Wydziału Zarządzania Politechniki Częstochowskiej Częstochowa 2016 Wydawnictwo Wydziału Zarządzania Politechniki Częstochowskiej 42-200 Częstochowa, al. Armii Krajowej 36 B tel. 34 325 04 80, dystrybucja 34 325 08 67 e-mail: [email protected] 2 Mathematical Modelling in Physics and Engineering The conference Mathematical Modelling in Physics and Engineering – MMPE’16 is organized by Czestochowa Branch of Polish Mathematical Society jointly with the Institute of Mathematics of Czestochowa University of Technology. Mathematical modelling is at the core of contemporary research within a wide range of fields of science
    [Show full text]