Numerical Solution of Partial Differential Equations
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Finite Difference Methods for Solving Differential
FINITE DIFFERENCE METHODS FOR SOLVING DIFFERENTIAL EQUATIONS I-Liang Chern Department of Mathematics National Taiwan University May 16, 2013 2 Contents 1 Introduction 3 1.1 Finite Difference Approximation . ........ 3 1.2 Basic Numerical Methods for Ordinary Differential Equations ........... 5 1.3 Runge-Kuttamethods..... ..... ...... ..... ...... ... ... 8 1.4 Multistepmethods ................................ 10 1.5 Linear difference equation . ...... 14 1.6 Stabilityanalysis ............................... 17 1.6.1 ZeroStability................................. 18 2 Finite Difference Methods for Linear Parabolic Equations 23 2.1 Finite Difference Methods for the Heat Equation . ........... 23 2.1.1 Some discretization methods . 23 2.1.2 Stability and Convergence for the Forward Euler method.......... 25 2.2 L2 Stability – von Neumann Analysis . 26 2.3 Energymethod .................................... 28 2.4 Stability Analysis for Montone Operators– Entropy Estimates ........... 29 2.5 Entropy estimate for backward Euler method . ......... 30 2.6 ExistenceTheory ................................. 32 2.6.1 Existence via forward Euler method . ..... 32 2.6.2 A Sharper Energy Estimate for backward Euler method . ......... 33 2.7 Relaxationoferrors.............................. 34 2.8 BoundaryConditions ..... ..... ...... ..... ...... ... 36 2.8.1 Dirichlet boundary condition . ..... 36 2.8.2 Neumann boundary condition . 37 2.9 The discrete Laplacian and its inversion . .......... 38 2.9.1 Dirichlet boundary condition . ..... 38 3 Finite Difference Methods for Linear elliptic Equations 41 3.1 Discrete Laplacian in two dimensions . ........ 41 3.1.1 Discretization methods . 41 3.1.2 The 9-point discrete Laplacian . ..... 42 3.2 Stability of the discrete Laplacian . ......... 43 3 4 CONTENTS 3.2.1 Fouriermethod ................................ 43 3.2.2 Energymethod ................................ 44 4 Finite Difference Theory For Linear Hyperbolic Equations 47 4.1 A review of smooth theory of linear hyperbolic equations ............. -
Exercises from Finite Difference Methods for Ordinary and Partial
Exercises from Finite Difference Methods for Ordinary and Partial Differential Equations by Randall J. LeVeque SIAM, Philadelphia, 2007 http://www.amath.washington.edu/ rjl/fdmbook ∼ Under construction | more to appear. Contents Chapter 1 4 Exercise 1.1 (derivation of finite difference formula) . 4 Exercise 1.2 (use of fdstencil) . 4 Chapter 2 5 Exercise 2.1 (inverse matrix and Green's functions) . 5 Exercise 2.2 (Green's function with Neumann boundary conditions) . 5 Exercise 2.3 (solvability condition for Neumann problem) . 5 Exercise 2.4 (boundary conditions in bvp codes) . 5 Exercise 2.5 (accuracy on nonuniform grids) . 6 Exercise 2.6 (ill-posed boundary value problem) . 6 Exercise 2.7 (nonlinear pendulum) . 7 Chapter 3 8 Exercise 3.1 (code for Poisson problem) . 8 Exercise 3.2 (9-point Laplacian) . 8 Chapter 4 9 Exercise 4.1 (Convergence of SOR) . 9 Exercise 4.2 (Forward vs. backward Gauss-Seidel) . 9 Chapter 5 11 Exercise 5.1 (Uniqueness for an ODE) . 11 Exercise 5.2 (Lipschitz constant for an ODE) . 11 Exercise 5.3 (Lipschitz constant for a system of ODEs) . 11 Exercise 5.4 (Duhamel's principle) . 11 Exercise 5.5 (matrix exponential form of solution) . 11 Exercise 5.6 (matrix exponential form of solution) . 12 Exercise 5.7 (matrix exponential for a defective matrix) . 12 Exercise 5.8 (Use of ode113 and ode45) . 12 Exercise 5.9 (truncation errors) . 13 Exercise 5.10 (Derivation of Adams-Moulton) . 13 Exercise 5.11 (Characteristic polynomials) . 14 Exercise 5.12 (predictor-corrector methods) . 14 Exercise 5.13 (Order of accuracy of Runge-Kutta methods) . -
Introduction to Differential Equations
Introduction to Differential Equations Lecture notes for MATH 2351/2352 Jeffrey R. Chasnov k kK m m x1 x2 The Hong Kong University of Science and Technology The Hong Kong University of Science and Technology Department of Mathematics Clear Water Bay, Kowloon Hong Kong Copyright ○c 2009–2016 by Jeffrey Robert Chasnov This work is licensed under the Creative Commons Attribution 3.0 Hong Kong License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/hk/ or send a letter to Creative Commons, 171 Second Street, Suite 300, San Francisco, California, 94105, USA. Preface What follows are my lecture notes for a first course in differential equations, taught at the Hong Kong University of Science and Technology. Included in these notes are links to short tutorial videos posted on YouTube. Much of the material of Chapters 2-6 and 8 has been adapted from the widely used textbook “Elementary differential equations and boundary value problems” by Boyce & DiPrima (John Wiley & Sons, Inc., Seventh Edition, ○c 2001). Many of the examples presented in these notes may be found in this book. The material of Chapter 7 is adapted from the textbook “Nonlinear dynamics and chaos” by Steven H. Strogatz (Perseus Publishing, ○c 1994). All web surfers are welcome to download these notes, watch the YouTube videos, and to use the notes and videos freely for teaching and learning. An associated free review book with links to YouTube videos is also available from the ebook publisher bookboon.com. I welcome any comments, suggestions or corrections sent by email to [email protected]. -
3 Runge-Kutta Methods
3 Runge-Kutta Methods In contrast to the multistep methods of the previous section, Runge-Kutta methods are single-step methods — however, with multiple stages per step. They are motivated by the dependence of the Taylor methods on the specific IVP. These new methods do not require derivatives of the right-hand side function f in the code, and are therefore general-purpose initial value problem solvers. Runge-Kutta methods are among the most popular ODE solvers. They were first studied by Carle Runge and Martin Kutta around 1900. Modern developments are mostly due to John Butcher in the 1960s. 3.1 Second-Order Runge-Kutta Methods As always we consider the general first-order ODE system y0(t) = f(t, y(t)). (42) Since we want to construct a second-order method, we start with the Taylor expansion h2 y(t + h) = y(t) + hy0(t) + y00(t) + O(h3). 2 The first derivative can be replaced by the right-hand side of the differential equation (42), and the second derivative is obtained by differentiating (42), i.e., 00 0 y (t) = f t(t, y) + f y(t, y)y (t) = f t(t, y) + f y(t, y)f(t, y), with Jacobian f y. We will from now on neglect the dependence of y on t when it appears as an argument to f. Therefore, the Taylor expansion becomes h2 y(t + h) = y(t) + hf(t, y) + [f (t, y) + f (t, y)f(t, y)] + O(h3) 2 t y h h = y(t) + f(t, y) + [f(t, y) + hf (t, y) + hf (t, y)f(t, y)] + O(h3(43)). -
Runge-Kutta Scheme Takes the Form K1 = Hf (Tn, Yn); K2 = Hf (Tn + Αh, Yn + Βk1); (5.11) Yn+1 = Yn + A1k1 + A2k2
Approximate integral using the trapezium rule: h Y (t ) ≈ Y (t ) + [f (t ; Y (t )) + f (t ; Y (t ))] ; t = t + h: n+1 n 2 n n n+1 n+1 n+1 n Use Euler's method to approximate Y (tn+1) ≈ Y (tn) + hf (tn; Y (tn)) in trapezium rule: h Y (t ) ≈ Y (t ) + [f (t ; Y (t )) + f (t ; Y (t ) + hf (t ; Y (t )))] : n+1 n 2 n n n+1 n n n Hence the modified Euler's scheme 8 K1 = hf (tn; yn) > h <> y = y + [f (t ; y ) + f (t ; y + hf (t ; y ))] , K2 = hf (tn+1; yn + K1) n+1 n 2 n n n+1 n n n > K1 + K2 :> y = y + n+1 n 2 5.3.1 Modified Euler Method Numerical solution of Initial Value Problem: dY Z tn+1 = f (t; Y ) , Y (tn+1) = Y (tn) + f (t; Y (t)) dt: dt tn Use Euler's method to approximate Y (tn+1) ≈ Y (tn) + hf (tn; Y (tn)) in trapezium rule: h Y (t ) ≈ Y (t ) + [f (t ; Y (t )) + f (t ; Y (t ) + hf (t ; Y (t )))] : n+1 n 2 n n n+1 n n n Hence the modified Euler's scheme 8 K1 = hf (tn; yn) > h <> y = y + [f (t ; y ) + f (t ; y + hf (t ; y ))] , K2 = hf (tn+1; yn + K1) n+1 n 2 n n n+1 n n n > K1 + K2 :> y = y + n+1 n 2 5.3.1 Modified Euler Method Numerical solution of Initial Value Problem: dY Z tn+1 = f (t; Y ) , Y (tn+1) = Y (tn) + f (t; Y (t)) dt: dt tn Approximate integral using the trapezium rule: h Y (t ) ≈ Y (t ) + [f (t ; Y (t )) + f (t ; Y (t ))] ; t = t + h: n+1 n 2 n n n+1 n+1 n+1 n Hence the modified Euler's scheme 8 K1 = hf (tn; yn) > h <> y = y + [f (t ; y ) + f (t ; y + hf (t ; y ))] , K2 = hf (tn+1; yn + K1) n+1 n 2 n n n+1 n n n > K1 + K2 :> y = y + n+1 n 2 5.3.1 Modified Euler Method Numerical solution of Initial Value Problem: dY Z tn+1 = -
The Original Euler's Calculus-Of-Variations Method: Key
Submitted to EJP 1 Jozef Hanc, [email protected] The original Euler’s calculus-of-variations method: Key to Lagrangian mechanics for beginners Jozef Hanca) Technical University, Vysokoskolska 4, 042 00 Kosice, Slovakia Leonhard Euler's original version of the calculus of variations (1744) used elementary mathematics and was intuitive, geometric, and easily visualized. In 1755 Euler (1707-1783) abandoned his version and adopted instead the more rigorous and formal algebraic method of Lagrange. Lagrange’s elegant technique of variations not only bypassed the need for Euler’s intuitive use of a limit-taking process leading to the Euler-Lagrange equation but also eliminated Euler’s geometrical insight. More recently Euler's method has been resurrected, shown to be rigorous, and applied as one of the direct variational methods important in analysis and in computer solutions of physical processes. In our classrooms, however, the study of advanced mechanics is still dominated by Lagrange's analytic method, which students often apply uncritically using "variational recipes" because they have difficulty understanding it intuitively. The present paper describes an adaptation of Euler's method that restores intuition and geometric visualization. This adaptation can be used as an introductory variational treatment in almost all of undergraduate physics and is especially powerful in modern physics. Finally, we present Euler's method as a natural introduction to computer-executed numerical analysis of boundary value problems and the finite element method. I. INTRODUCTION In his pioneering 1744 work The method of finding plane curves that show some property of maximum and minimum,1 Leonhard Euler introduced a general mathematical procedure or method for the systematic investigation of variational problems. -
Finite Difference and Discontinuous Galerkin Methods for Wave Equations
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology 1522 Finite Difference and Discontinuous Galerkin Methods for Wave Equations SIYANG WANG ACTA UNIVERSITATIS UPSALIENSIS ISSN 1651-6214 ISBN 978-91-554-9927-3 UPPSALA urn:nbn:se:uu:diva-320614 2017 Dissertation presented at Uppsala University to be publicly examined in Room 2446, Polacksbacken, Lägerhyddsvägen 2, Uppsala, Tuesday, 13 June 2017 at 10:15 for the degree of Doctor of Philosophy. The examination will be conducted in English. Faculty examiner: Professor Thomas Hagstrom (Department of Mathematics, Southern Methodist University). Abstract Wang, S. 2017. Finite Difference and Discontinuous Galerkin Methods for Wave Equations. Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology 1522. 53 pp. Uppsala: Acta Universitatis Upsaliensis. ISBN 978-91-554-9927-3. Wave propagation problems can be modeled by partial differential equations. In this thesis, we study wave propagation in fluids and in solids, modeled by the acoustic wave equation and the elastic wave equation, respectively. In real-world applications, waves often propagate in heterogeneous media with complex geometries, which makes it impossible to derive exact solutions to the governing equations. Alternatively, we seek approximated solutions by constructing numerical methods and implementing on modern computers. An efficient numerical method produces accurate approximations at low computational cost. There are many choices of numerical methods for solving partial differential equations. Which method is more efficient than the others depends on the particular problem we consider. In this thesis, we study two numerical methods: the finite difference method and the discontinuous Galerkin method. -
Numerical Stability; Implicit Methods
NUMERICAL STABILITY; IMPLICIT METHODS When solving the initial value problem 0 Y (x) = f (x; Y (x)); x0 ≤ x ≤ b Y (x0) = Y0 we know that small changes in the initial data Y0 will result in small changes in the solution of the differential equation. More precisely, consider the perturbed problem 0 Y"(x) = f (x; Y"(x)); x0 ≤ x ≤ b Y"(x0) = Y0 + " Then assuming f (x; z) and @f (x; z)=@z are continuous for x0 ≤ x ≤ b; −∞ < z < 1, we have max jY"(x) − Y (x)j ≤ c j"j x0≤x≤b for some constant c > 0. We would like our numerical methods to have a similar property. Consider the Euler method yn+1 = yn + hf (xn; yn) ; n = 0; 1;::: y0 = Y0 and then consider the perturbed problem " " " yn+1 = yn + hf (xn; yn ) ; n = 0; 1;::: " y0 = Y0 + " We can show the following: " max jyn − ynj ≤ cbj"j x0≤xn≤b for some constant cb > 0 and for all sufficiently small values of the stepsize h. This implies that Euler's method is stable, and in the same manner as was true for the original differential equation problem. The general idea of stability for a numerical method is essentially that given above for Eulers's method. There is a general theory for numerical methods for solving the initial value problem 0 Y (x) = f (x; Y (x)); x0 ≤ x ≤ b Y (x0) = Y0 If the truncation error in a numerical method has order 2 or greater, then the numerical method is stable if and only if it is a convergent numerical method. -
Leonhard Euler: His Life, the Man, and His Works∗
SIAM REVIEW c 2008 Walter Gautschi Vol. 50, No. 1, pp. 3–33 Leonhard Euler: His Life, the Man, and His Works∗ Walter Gautschi† Abstract. On the occasion of the 300th anniversary (on April 15, 2007) of Euler’s birth, an attempt is made to bring Euler’s genius to the attention of a broad segment of the educated public. The three stations of his life—Basel, St. Petersburg, andBerlin—are sketchedandthe principal works identified in more or less chronological order. To convey a flavor of his work andits impact on modernscience, a few of Euler’s memorable contributions are selected anddiscussedinmore detail. Remarks on Euler’s personality, intellect, andcraftsmanship roundout the presentation. Key words. LeonhardEuler, sketch of Euler’s life, works, andpersonality AMS subject classification. 01A50 DOI. 10.1137/070702710 Seh ich die Werke der Meister an, So sehe ich, was sie getan; Betracht ich meine Siebensachen, Seh ich, was ich h¨att sollen machen. –Goethe, Weimar 1814/1815 1. Introduction. It is a virtually impossible task to do justice, in a short span of time and space, to the great genius of Leonhard Euler. All we can do, in this lecture, is to bring across some glimpses of Euler’s incredibly voluminous and diverse work, which today fills 74 massive volumes of the Opera omnia (with two more to come). Nine additional volumes of correspondence are planned and have already appeared in part, and about seven volumes of notebooks and diaries still await editing! We begin in section 2 with a brief outline of Euler’s life, going through the three stations of his life: Basel, St. -
A Quasi-Static Particle-In-Cell Algorithm Based on an Azimuthal Fourier Decomposition for Highly Efficient Simulations of Plasma-Based Acceleration
A quasi-static particle-in-cell algorithm based on an azimuthal Fourier decomposition for highly efficient simulations of plasma-based acceleration: QPAD Fei Lia,c, Weiming And,∗, Viktor K. Decykb, Xinlu Xuc, Mark J. Hoganc, Warren B. Moria,b aDepartment of Electrical Engineering, University of California Los Angeles, Los Angeles, CA 90095, USA bDepartment of Physics and Astronomy, University of California Los Angeles, Los Angeles, CA 90095, USA cSLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA dDepartment of Astronomy, Beijing Normal University, Beijing 100875, China Abstract The three-dimensional (3D) quasi-static particle-in-cell (PIC) algorithm is a very effi- cient method for modeling short-pulse laser or relativistic charged particle beam-plasma interactions. In this algorithm, the plasma response, i.e., plasma wave wake, to a non- evolving laser or particle beam is calculated using a set of Maxwell's equations based on the quasi-static approximate equations that exclude radiation. The plasma fields are then used to advance the laser or beam forward using a large time step. The algorithm is many orders of magnitude faster than a 3D fully explicit relativistic electromagnetic PIC algorithm. It has been shown to be capable to accurately model the evolution of lasers and particle beams in a variety of scenarios. At the same time, an algorithm in which the fields, currents and Maxwell equations are decomposed into azimuthal harmonics has been shown to reduce the complexity of a 3D explicit PIC algorithm to that of a 2D algorithm when the expansion is truncated while maintaining accuracy for problems with near azimuthal symmetry. -
A Numerical Method of Characteristics for Solving Hyperbolic Partial Differential Equations David Lenz Simpson Iowa State University
Iowa State University Capstones, Theses and Retrospective Theses and Dissertations Dissertations 1967 A numerical method of characteristics for solving hyperbolic partial differential equations David Lenz Simpson Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/rtd Part of the Mathematics Commons Recommended Citation Simpson, David Lenz, "A numerical method of characteristics for solving hyperbolic partial differential equations " (1967). Retrospective Theses and Dissertations. 3430. https://lib.dr.iastate.edu/rtd/3430 This Dissertation is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Retrospective Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. This dissertation has been microfilmed exactly as received 68-2862 SIMPSON, David Lenz, 1938- A NUMERICAL METHOD OF CHARACTERISTICS FOR SOLVING HYPERBOLIC PARTIAL DIFFERENTIAL EQUATIONS. Iowa State University, PluD., 1967 Mathematics University Microfilms, Inc., Ann Arbor, Michigan A NUMERICAL METHOD OF CHARACTERISTICS FOR SOLVING HYPERBOLIC PARTIAL DIFFERENTIAL EQUATIONS by David Lenz Simpson A Dissertation Submitted to the Graduate Faculty in Partial Fulfillment of The Requirements for the Degree of DOCTOR OF PHILOSOPHY Major Subject: Mathematics Approved: Signature was redacted for privacy. In Cijarge of Major Work Signature was redacted for privacy. Headead of^ajorof Major DepartmentDepartmen Signature was redacted for privacy. Dean Graduate Co11^^ Iowa State University of Science and Technology Ames, Iowa 1967 ii TABLE OF CONTENTS Page I. INTRODUCTION 1 II. DISCUSSION OF THE ALGORITHM 3 III. DISCUSSION OF LOCAL ERROR, EXISTENCE AND UNIQUENESS THEOREMS 10 IV. -
Hydroinformatik II ”Prozess-Simulation Und Systemanalyse”
Version 6.01 - 5. Mai 2017 Hydroinformatik II ”Prozess-Simulation und Systemanalyse” Prof. Dr.-Ing. Olaf Kolditz TU Dresden / UFZ Leipzig Angewandte Umweltsystemanalyse Department Umweltinformatik Sommersemester 2015 2 Vorlesungskonzept Es geht weiter ... Nachdem wir uns im erstem Semester Hydroinformatik mit dem objekt-orientierten Programmierung mit C++ ”angefreundet” haben, soll es in diesem Semester auch um die Anwendung in den Disziplinen Hydrologie, Wasser- und Abfallwirtschaft gehen. Naturlich¨ werden wir uns mit sehr einfa- chen Beispielen besch¨aftigen (und z.B. keine kompletten Kl¨aranlagen rechnen, dafur¨ sind die Kollegen von der Siedlungswasserwirtschaft zust¨andig). Dennoch wollen wir ihnen am Ende des Semesters auch einen Einblick in unsere laufende Forschung geben, z.B. den Einsatz von numerischen Modellen bei der Abfallla- gerung [11], in der Hydrologie [5] und in der Grundwassermodellierung [15]. Der Vorlesungsfahrplan sieht wir folgt aus: Grundlagen - Mechanik (2-3V) • Grundlagen - Numerik (2-3V) • Prozessverst¨andnis - Benchmarks (3V) • Anwendungen (2V) • Visual C++ mit Qt (4V) • Abbildung 1: Lehrbuch zur ”Numerischen Hydromechanik” [8] 3 Grundlagen Fur¨ die ersten beiden Teile: Mechanik und Numerik, verwende ich weitgehend Material aus meinem Springer-Lehrbuch ”Numerical Methods in Environmental Fluid Mechanics” [8] (fur¨ Bauingenieure, siehe Abb. 1). Als deutsprachige Lite- ratur empfehle ich ihnen naturlich¨ die Dresdner Klassiker [7] und [2] sowie [3] (Hydromechanik, generell) [12] (Gerinnehydraulik), [6] (Grundwasserhydraulik).