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Partial Differential Equations: A First Course

Rustum Choksi McGill University, Montreal, QC, Canada
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Softcover ISBN: 978-1-4704-6491-2
Product Code: AMSTEXT/54
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AMS Member Price: $106.80 Click above image for expanded view Partial Differential Equations: A First Course Rustum Choksi McGill University, Montreal, QC, Canada Available Formats:  Softcover ISBN: 978-1-4704-6491-2 Product Code: AMSTEXT/54  List Price:$89.00 MAA Member Price: $80.10 AMS Member Price:$71.20
 Electronic ISBN: 978-1-4704-6867-5 Product Code: AMSTEXT/54.E
 List Price: $89.00 MAA Member Price:$80.10 AMS Member Price: $71.20 Bundle Print and Electronic Formats and Save! This product is available for purchase as a bundle. Purchasing as a bundle enables you to save on the electronic version.  List Price:$133.50 MAA Member Price: $120.15 AMS Member Price:$106.80
• Book Details

Pure and Applied Undergraduate Texts
Volume: 542022; 612 pp
MSC: Primary 35;

While partial differential equations (PDEs) are fundamental in mathematics and throughout the sciences, most undergraduate students are only exposed to PDEs through the method of separation of variations. This text is written for undergraduate students from different cohorts with one sole purpose: to facilitate a proficiency in many core concepts in PDEs while enhancing the intuition and appreciation of the subject. For mathematics students this will in turn provide a solid foundation for graduate study. A recurring theme is the role of concentration as captured by Dirac's delta function. This both guides the student into the structure of the solution to the diffusion equation and PDEs involving the Laplacian and invites them to develop a cognizance for the theory of distributions. Both distributions and the Fourier transform are given full treatment.

The book is rich with physical motivations and interpretations, and it takes special care to clearly explain all the technical mathematical arguments, often with pre-motivations and post-reflections. Through these arguments the reader will develop a deeper proficiency and understanding of advanced calculus. While the text is comprehensive, the material is divided into short sections, allowing particular issues/topics to be addressed in a concise fashion. Sections which are more fundamental to the text are highlighted, allowing the instructor several alternative learning paths. The author's unique pedagogical style also makes the text ideal for self-learning.

Undergraduate and graduate students interested in partial differential equations.

• Preface
• 0.1. Who Should Take a First Course in PDEs?
• 0.2. A Text for All Three Groups: Grounding in Core Concepts and Topics
• 0.3. Basic Structure of the Text
• 0.3.1. Presentation and Modularization of Material
• 0.3.2. Choice for the Orderings of Chapters
• 0.3.3. Codependence and Different Orderings of Chapters
• 0.4. Prerequisites
• 0.4.1. Advanced Calculus and the Appendix
• 0.4.2. Breadth and Nonrigidity
• 0.5. Acknowledgments
• Chapter 1. Basic Definitions
• 1.1. ∙ Notation
• 1.2. ∙ What Are Partial Differential Equations and Why Are They Ubiquitous?
• 1.3. ∙ What Exactly Do We Mean by a Solution to a PDE?
• 1.4. ∙ Order, Linear vs. Nonlinear, Scalar vs. Systems
• 1.4.1. ∙ Order
• 1.4.2. ∙ Linear vs. Nonlinear
• 1.4.3. ∙ The Principle of Superposition for Linear PDEs
• 1.4.4. ∙ Scalar vs. Systems
• 1.5. ∙ General Solutions, Arbitrary Functions, Auxiliary Conditions, and the Notion of a Well-Posed Problem
• 1.5.1. ∙ General Solutions and Arbitrary Functions
• 1.5.2. ∙ Auxiliary Conditions: Boundary and Initial Conditions
• 1.5.3. ∙ General Auxiliary Conditions and the Cauchy Problem
• 1.5.4. ∙ A Well-Posed Problem
• 1.6. ∙ Common Approaches and Themes in Solving PDEs
• 1.6.1. ∙ Assuming We Have a Solution and Going Forward
• 1.6.2. ∙ Explicit vs. Nonexplicit Solutions, No Solutions, and Approximate Solutions
• Exercises
• Chapter 2. First-Order PDEs and the Method of Characteristics
• 2.1. ∙ Prelude: A Few Simple Examples Illustrating the Notion and Geometry of Characteristics
• 2.2. ∙ The Method of Characteristics, Part I: Linear Equations
• 2.2.1. ∙ A Few Examples
• 2.2.2. ∙ Temporal Equations: Using Time to Parametrize the Characteristics
• 2.2.3. ∙ More Than Two Independent Variables
• 2.2.4. ∙ Transport Equations in Three Space Dimensions with Constant Velocity
• 2.2.5. Transport Equations in Three Space Dimensions with Space Varying Velocity
• 2.2.6. The Continuity Equation in Three Space Dimensions: A Derivation
• 2.2.7. Semilinear Equations
• 2.2.8. Noncharacteristic Data and the Transversality Condition
• 2.3. ∙ An Important Quasilinear Example: The Inviscid Burgers Equation
• 2.4. ∙ The Method of Characteristics, Part II: Quasilinear Equations
• 2.4.1. ∙ A Few Examples
• 2.4.2. ∙ More Than Two Independent Variables
• 2.4.3. Pausing to Reflect on the Inherent Logic Behind the Method of Characteristics and Local Solutions of Quasilinear Equations
• 2.5. The Method of Characteristics, Part III: General First-Order Equations
• 2.5.1. The Notation
• 2.5.2. The Characteristic Equations
• 2.5.3. Linear and Quasilinear Equations in 𝑁 independent variables
• 2.5.4. Two Fully Nonlinear Examples
• 2.5.5. The Eikonal Equation
• 2.5.6. Hamilton-Jacobi Equations
• 2.5.7. The Level Set Equation and Interface Motion
• 2.6. ∙ Some General Questions
• 2.7. ∙ A Touch of Numerics, I: Computing the Solution of the Transport Equation
• 2.7.1. ∙ Three Consistent Schemes
• 2.7.2. ∙ von Neumann Stability Analysis
• 2.8. The Euler Equations: A Derivation
• 2.8.1. Conservation of Mass and the Continuity Equation
• 2.8.2. Conservation of Linear Momentum and Pressure
• 2.8.3. Gas Dynamics: The Compressible Euler Equations
• 2.8.4. An Ideal Liquid: The Incompressible Euler Equations
• 2.8.5. A Viscous Liquid: The Navier-Stokes Equations
• 2.8.6. Spatial vs. Material Coordinates and the Material Time Derivative
• 2.9. Chapter Summary
• Exercises
• Chapter 3. The Wave Equation in One Space Dimension
• 3.1. ∙ Derivation: A Vibrating String
• 3.2. ∙ The General Solution of the 1D Wave Equation
• 3.3. ∙ The Initial Value Problem and Its Explicit Solution: D’Alembert’s Formula
• 3.4. ∙ Consequences of D’Alembert’s Formula: Causality
• 3.4.1. ∙ The Domain of Dependence and Influence
• 3.4.2. ∙ Two Examples: A Plucked String and a Hammer Blow
• 3.5. ∙ Conservation of the Total Energy
• 3.6. ∙ Sources
• 3.6.1. ∙ Duhamel’s Principle
• 3.6.2. Derivation via Green’s Theorem
• 3.7. ∙ Well-Posedness of the Initial Value Problem and Time Reversibility
• 3.8. ∙ The Wave Equation on the Half-Line with a Fixed Boundary: Reflections
• 3.8.1. ∙ A Dirichlet (Fixed End) Boundary Condition and Odd Reflections
• 3.8.2. ∙ Causality with Respect to the Fixed Boundary
• 3.8.3. ∙ The Plucked String and Hammer Blow Examples with a Fixed Left End
• 3.9. ∙ Neumann and Robin Boundary Conditions
• 3.10. ∙ Finite String Boundary Value Problems
• 3.11. ∙ A Touch of Numerics, II: Numerical Solution to the Wave Equation
• 3.12. Some Final Remarks
• 3.12.1. Nonsmooth Solutions
• 3.12.2. Heterogeneous Media and Scattering
• 3.12.3. Finite Propagation Speed, Other “Wave” Equations, and Dispersion
• 3.12.4. Characterizing Dispersion in PDEs via the Ubiquitous Traveling Wave Solution
• 3.13. Chapter Summary
• Exercises
• Chapter 4. The Wave Equation in Three and Two Space Dimensions
• 4.1. ∙ Two Derivations of the 3D Wave Equation
• 4.1.1. ∙ Derivation 1: Electromagnetic Waves from Maxwell’s Equations
• 4.1.2. Derivation 2: Acoustics from the Euler Equations
• 4.2. ∙ Three Space Dimensions: The Initial Value Problem and Its Explicit Solution
• 4.2.1. ∙ Kirchhoff’s Formula
• 4.2.2. ∙ Consequences of Kirchhoff’s Formula: Causality and the Huygens Principle
• 4.2.3. ∙ Kirchhoff’s Formula via Spherical Means
• 4.2.4. Full Details: The Proof of Kirchhoff’s Formula
• 4.3. Two Space Dimensions: The 2D Wave Equation and Its Explicit Solution
• 4.3.1. The Solution via the Method of Descent
• 4.3.2. Causality in 2D
• 4.4. Some Final Remarks and Geometric Optics
• 4.4.1. The Wave Equation in Space Dimensions Larger Than Three
• 4.4.2. Regularity
• 4.4.3. Nonconstant Coefficients and Inverse Problems
• 4.4.4. Geometric Optics and the Eikonal Equation
• 4.5. Chapter Summary
• Exercises
• Chapter 5. The Delta “Function” and Distributions in One Space Dimension
• 5.1. ∙ Real-Valued Functions
• 5.1.1. ∙ What Is a Function?
• 5.1.2. ∙ Why Integrals (or Averages) of a Function Trump Pointwise Values
• 5.1.3. ∙ Singularities of Functions from the Point of View of Averages
• 5.2. ∙ The Delta “Function” and Why It Is Not a Function. Motivation for Generalizing the Notion of a Function
• 5.2.1. ∙ The Delta “Function” and the Derivative of the Heaviside Function
• 5.2.2. ∙ The Delta “Function” as a Limit of a Sequence of Functions Which Concentrate
• 5.3. ∙ Distributions (Generalized Functions)
• 5.3.1. ∙ The Class of Test Functions 𝐶_{𝑐}^{∞}(ℝ)
• 5.3.2. ∙ The Definition of a Distribution
• 5.3.3. ∙ Functions as Distributions
• 5.3.4. ∙ The Precise Definition of the Delta “Function” as a Distribution
• 5.4. ∙ Derivative of a Distribution
• 5.4.1. ∙ Motivation via Integration by Parts of Differentiable Functions
• 5.4.2. ∙ The Definition of the Derivative of a Distribution
• 5.4.3. ∙ Examples of Derivatives of Piecewise Smooth Functions in the Sense of Distributions
• 5.5. ∙ Convergence in the Sense of Distributions
• 5.5.1. ∙ The Definition
• 5.5.2. ∙ Comparisons of Distributional versus Pointwise Convergence of Functions
• 5.5.3. ∙ The Distributional Convergence of a Sequence of Functions to the Delta Function: Four Examples
• 5.5.4. 𝜀 vs. 𝑁 Proofs for the Sequences (5.23) and (5.24)
• 5.5.5. ∙ The Distributional Convergence of sin𝑛𝑥
• 5.5.6. ∙ The Distributional Convergence of the Sinc Functions and the Dirichlet Kernel: Two Sequences Directly Related to Fourier Analysis
• 5.6. Dirac’s Intuition: Algebraic Manipulations with the Delta Function
• 5.6.1. Rescaling and Composition with Polynomials
• 5.6.2. Products of Delta Functions in Different Variables
• 5.6.3. Symmetry in the Argument
• 5.7. ∙ Distributions Defined on an Open Interval and Larger Classes of Test Functions
• 5.7.1. ∙ Distributions Defined over a Domain
• 5.7.2. ∙ Larger Classes of Test Functions
• 5.8. Nonlocally Integrable Functions as Distributions: The Distribution PV 1/𝑥
• 5.8.1. Three Distributions Associated with the Function 1/𝑥
• 5.8.2. A Second Way to Write 𝑃𝑉1/𝑥
• 5.8.3. A Third Way to Write 𝑃𝑉1/𝑥
• 5.8.4. The Distributional Derivative of 𝑃𝑉1/𝑥
• 5.9. Chapter Summary
• Exercises
• Chapter 6. The Fourier Transform
• 6.1. ∙ Complex Numbers
• 6.2. ∙ Definition of the Fourier Transform and Its Fundamental Properties
• 6.2.1. ∙ The Definition
• 6.2.2. ∙ Differentiation and the Fourier Transform
• 6.2.3. ∙ Fourier Inversion via the Delta Function
• 6.2.4. Finding the Fourier and Inverse Fourier Transforms of Particular Functions
• 6.2.5. The Fourier Transform of a Complex-Valued Function
• 6.3. ∙ Convolution of Functions and the Fourier Transform
• 6.3.1. ∙ The Definition of Convolution
• 6.3.2. ∙ Differentiation of Convolutions
• 6.3.3. ∙ Convolution and the Fourier Transform
• 6.3.4. Convolution as a Way of Smoothing Out Functions and Generating Test Functions in 𝐶^{∞}_{𝑐}
• 6.4. ∙ Other Important Properties of the Fourier Transform
• 6.5. Duality: Decay at Infinity vs. Smoothness
• 6.6. Plancherel’s Theorem and the Riemann-Lebesgue Lemma
• 6.6.1. The Spaces 𝐿¹(ℝ) and 𝕃²(ℝ)
• 6.6.2. Extending the Fourier Transform to Square-Integrable Functions and Plancherel’s Theorem
• 6.6.3. The Riemann-Lebesgue Lemma
• 6.7. The 2𝜋 Issue and Other Possible Definitions of the Fourier Transform
• 6.8. ∙ Using the Fourier Transform to Solve Linear PDEs, I: The Diffusion Equation
• 6.8.1. ∙ Prelude: Using the Fourier Transform to Solve a Linear ODE
• 6.8.2. ∙ Using the Fourier Transform to Solve the Diffusion Equation
• 6.9. The Fourier Transform of a Tempered Distribution
• 6.9.1. Can We Extend the Fourier Transform to Distributions?
• 6.9.2. The Schwartz Class of Test Functions and Tempered Distributions
• 6.9.3. The Fourier Transform of 𝑓(𝑥)≡1 and the Delta Function
• 6.9.4. The Fourier Transform of 𝑓(𝑥)=𝑒^{𝑖𝑎𝑥} and the Delta Function 𝛿ₐ
• 6.9.5. The Fourier Transform of Sine, Cosine, and Sums of Delta Functions
• 6.9.6. The Fourier Transform of 𝑥
• 6.9.7. The Fourier Transform of the Heaviside and Sgn Functions and PV 1/𝑥
• 6.9.8. Convolution of a Tempered Distribution and a Function
• 6.10. Using the Fourier Transform to Solve PDEs, II: The Wave Equation
• 6.11. The Fourier Transform in Higher Space Dimensions
• 6.11.1. Definition and Analogous Properties
• 6.11.2. The Fourier Transform of a Radially Symmetric Function and Bessel Functions
• 6.12. Frequency, Harmonics, and the Physical Meaning of the Fourier Transform
• 6.12.1. Plane Waves in One Dimension
• 6.12.2. Interpreting the Fourier Inversion Formula
• 6.12.3. Revisiting the Wave Equation
• 6.12.4. Properties of the Fourier Transform, Revisited
• 6.12.5. The Uncertainty Principle
• 6.13. A Few Words on Other Transforms
• 6.14. Chapter Summary
• 6.15. Summary Tables
• Exercises
• Chapter 7. The Diffusion Equation
• 7.1. ∙ Derivation 1: Fourier’s/Fick’s Law
• 7.2. ∙ Solution in One Space Dimension and Properties
• 7.2.1. ∙ The Fundamental Solution/Heat Kernel and Its Properties
• 7.2.2. ∙ Properties of the Solution Formula
• 7.2.3. The Proof for the Solution to the Initial Value Problem: The Initial Conditions and the Delta Function
• 7.3. ∙ Derivation 2: Limit of Random Walks
• 7.3.1. ∙ Numerical Approximation of Second Derivatives
• 7.3.2. ∙ Random Walks
• 7.3.3. ∙ The Fundamental Limit and the Diffusion Equation
• 7.3.4. ∙ The Limiting Dynamics: Brownian Motion
• 7.4. Solution via the Central Limit Theorem
• 7.4.1. Random Variables, Probability Densities and Distributions, and the Normal Distribution
• 7.4.2. The Central Limit Theorem
• 7.4.3. Application to Our Limit of Random Walks and the Solution to the Diffusion Equation
• 7.5. ∙ Well-Posedness of the IVP and Ill-Posedness of the Backward Diffusion Equation
• 7.5.1. ∙ Nonuniqueness of the IVP of the Diffusion Equation
• 7.5.2. ∙ Ill-Posedness of the Backward Diffusion Equation
• 7.5.3. Deblurring in Image Processing
• 7.6. ∙ Some Boundary Value Problems in the Context of Heat Flow
• 7.6.1. ∙ Dirichlet and Neumann Boundary Conditions
• 7.6.2. ∙ The Robin Condition and Heat Transfer
• 7.7. ∙ The Maximum Principle on a Finite Interval
• 7.8. Source Terms and Duhamel’s Principle Revisited
• 7.8.1. An Intuitive and Physical Explanation of Duhamel’s Principle for Heat Flow with a Source
• 7.9. The Diffusion Equation in Higher Space Dimensions
• 7.10. ∙ A Touch of Numerics, III: Numerical Solution to the Diffusion Equation
• 7.11. Addendum: The Schrödinger Equation
• 7.12. Chapter Summary
• Exercises
• Chapter 8. The Laplacian, Laplace’s Equation, and Harmonic Functions
• 8.1. ∙ The Dirichlet and Neumann Boundary Value Problems for Laplace’s and Poisson’s Equations
• 8.2. ∙ Derivation and Physical Interpretations 1: Concentrations in Equilibrium
• 8.3. Derivation and Physical Interpretations 2: The Dirichlet Problem and Poisson’s Equation via 2D Random Walks/Brownian Motion
• 8.4. ∙ Basic Properties of Harmonic Functions
• 8.4.1. ∙ The Mean Value Property
• 8.4.2. ∙ The Maximum Principle
• 8.4.3. ∙ The Dirichlet Principle
• 8.4.4. Smoothness (Regularity)
• 8.5. ∙ Rotational Invariance and the Fundamental Solution
• 8.6. ∙ The Discrete Form of Laplace’s Equation
• 8.7. The Eigenfunctions and Eigenvalues of the Laplacian
• 8.7.1. Eigenvalues and Energy: The Rayleigh Quotient
• 8.8. The Laplacian and Curvature
• 8.8.1. Principal Curvatures of a Surface
• 8.8.2. Mean Curvature
• 8.8.3. Curvature and Invariance
• 8.9. Chapter Summary
• Exercises
• Chapter 9. Distributions in Higher Dimensions and Partial Differentiation in the Sense of Distributions
• 9.1. ∙ The Test Functions and the Definition of a Distribution
• 9.2. ∙ Convergence in the Sense of Distributions
• 9.3. ∙ Partial Differentiation in the Sense of Distributions
• 9.3.1. ∙ The Notation and Definition
• 9.3.2. ∙ A 2D Jump Discontinuity Example
• 9.4. ∙ The Divergence and Curl in the Sense of Distributions: Two Important Examples
• 9.4.1. ∙ The Divergence of the Gravitational Vector Field
• 9.4.2. The Curl of a Canonical Vector Field
• 9.5. ∙ The Laplacian in the Sense of Distributions and a Fundamental Example
• 9.6. Distributions Defined on a Domain (with and without Boundary)
• 9.7. Interpreting Many PDEs in the Sense of Distributions
• 9.7.1. Our First Example Revisited!
• 9.7.2. Burgers’s Equation and the Rankine-Hugoniot Jump Conditions
• 9.7.3. The Wave Equation with a Delta Function Source
• 9.7.4. Incorporating Initial Values into a Distributional Solution
• 9.7.5. Not All PDEs Can Be Interpreted in the Sense of Distributions
• 9.8. A View Towards Sobolev Spaces
• 9.9. Fourier Transform of an 𝑁-dimensional Tempered Distribution
• 9.10. Using the Fourier Transform to Solve Linear PDEs, III: Helmholtz and Poisson Equations in Three Space
• 9.11. Chapter Summary
• Exercises
• Chapter 10. The Fundamental Solution and Green’s Functions for the Laplacian
• 10.1. ∙ The Proof for the Distributional Laplacian of 1over |𝐱|
• 10.2. ∙ Unlocking the Power of the Fundamental Solution for the Laplacian
• 10.2.1. ∙ The Fundamental Solution Is Key to Solving Poisson’s Equation
• 10.2.2. ∙ The Fundamental Solution Gives a Representation Formula for Any Harmonic Function in Terms of Boundary Data
• 10.3. ∙ Green’s Functions for the Laplacian with Dirichlet Boundary Conditions
• 10.3.1. ∙ The Definition of the Green’s Function with Dirichlet Boundary Conditions
• 10.3.2. Using the Green’s Function to Solve the Dirichlet Problem for Laplace’s Equation
• 10.3.3. ∙ Uniqueness and Symmetry of the Green’s Function
• 10.3.4. ∙ The Fundamental Solution and Green’s Functions in One Space Dimension
• 10.4. ∙ Green’s Functions for the Half-Space and Ball in 3D
• 10.4.1. ∙ Green’s Function for the Half-Space
• 10.4.2. ∙ Green’s Function for the Ball
• 10.4.3. The Proof for Theorem 10.9
• 10.4.4. Green’s Functions for Other Domains and Differential Operators
• 10.5. Green’s Functions for the Laplacian with Neumann Boundary Conditions
• 10.5.1. Finding the Neumann Green’s Function for the Half-Space in ℝ³
• 10.5.2. Finding the Neumann Green’s Function for the Ball in ℝ³
• 10.6. A Physical Illustration in Electrostatics: Coulomb’s Law, Gauss’s Law, the Electric Field, and Electrostatic Potential
• 10.6.1. Coulomb’s Law and the Electrostatic Force
• 10.6.2. The Electrostatic Potential: The Fundamental Solution and Poisson’s Equation
• 10.6.3. Green’s Functions: Grounded Conducting Plates, Induced Charge Densities, and the Method of Images
• 10.6.4. Interpreting the Solution Formula for the Dirichlet Problem
• 10.7. Chapter Summary
• Exercises
• Chapter 11. Fourier Series
• 11.1. ∙ Prelude: The Classical Fourier Series —the Fourier Sine Series, the Fourier Cosine Series, and the Full Fourier Series
• 11.1.1. ∙ The Fourier Sine Series
• 11.1.2. ∙ The Fourier Cosine Series
• 11.1.3. ∙ The Full Fourier Series
• 11.1.4. ∙ Three Examples
• 11.1.5. ∙ Viewing the Three Fourier Series as Functions over ℝ
• 11.1.6. ∙ Convergence, Boundary Values, Piecewise Continuity, and Periodic Extensions
• 11.1.7. Complex Version of the Full Fourier Series
• 11.2. ∙ Why Cosines and Sines? Eigenfunctions, Eigenvalues, and Orthogonality
• 11.2.1. ∙ Finite Dimensions —the Linear Algebra of Vectors
• 11.2.2. ∙ Infinite Dimensions —the Linear Algebra of Functions
• 11.2.3. ∙ The Linear Operator 𝒜=-𝒹²over 𝒹𝓍² and Symmetric Boundary Conditions
• 11.3. ∙ Fourier Series in Terms of Eigenfunctions of 𝒜 with a Symmetric Boundary Condition
• 11.3.1. ∙ The Eigenfunctions and Respective Fourier Series Associated with the Four Standard Symmetric Boundary Conditions
• 11.3.2. ∙ The Miracle: These Sets of Eigenfunctions Span the Space of All Reasonable Functions
• 11.4. ∙ Convergence, I: The 𝐿² Theory, Bessel’s Inequality, and Parseval’s Equality
• 11.4.1. ∙ 𝐿² Convergence of a Sequence of Functions
• 11.4.2. ∙ 𝐿² Convergence of Fourier Series
• 11.4.3. ∙ Bessel’s Inequality and Reducing the 𝐿² Convergence Theorem to Parseval’s Equality
• 11.4.4. The Riemann-Lebesgue Lemma and an Application of Parseval’s Equality
• 11.5. ∙ Convergence, II: The Dirichlet Kernel and Pointwise Convergence of the Full Fourier Series
• 11.5.1. ∙ Pointwise Convergence of a Sequence of Functions
• 11.5.2. ∙ Pointwise Convergence of the Full Fourier Series: The Dirichlet Kernel and the Delta Function
• 11.5.3. ∙ The Proof of Pointwise Convergence of the Full Fourier Series
• 11.6. Term-by-Term Differentiation and Integration of Fourier Series
• 11.6.1. Term-by-Term Differentiation
• 11.6.2. Term-by-Term Integration
• 11.7. Convergence, III: Uniform Convergence
• 11.7.1. Uniform Convergence of Functions
• 11.7.2. A Criterion for the Uniform Convergence of Fourier Series
• 11.7.3. The Proof of Theorem 11.9
• 11.7.4. The Gibbs Phenomenon
• 11.8. What Is the Relationship Between Fourier Series and the Fourier Transform?
• 11.8.1. Sending 𝑙→∞ in the Full Fourier Series
• 11.8.2. Taking the Distributional Fourier Transform of a Periodic Function
• 11.9. Chapter Summary
• Exercises
• Chapter 12. The Separation of Variables Algorithm for Boundary Value Problems
• 12.1. ∙ The Basic Separation of Variables Algorithm
• 12.1.1. ∙ The Diffusion Equation with Homogeneous Dirichlet Boundary Conditions
• 12.1.2. ∙ The Diffusion Equation with Homogenous Neumann Boundary Conditions
• 12.2. ∙ The Wave Equation
• 12.2.1. ∙ The Wave Equation with Homogeneous Dirichlet Boundary Conditions
• 12.2.2. The Wave Equation with Homogeneous Neumann Boundary Conditions
• 12.3. ∙ Other Boundary Conditions
• 12.3.1. ∙ Inhomogeneous Dirichlet Boundary Conditions
• 12.3.2. ∙ Mixed Homogeneous Boundary Conditions
• 12.3.3. ∙ Mixed Inhomogeneous Boundary Conditions
• 12.3.4. ∙ Inhomogeneous Neumann Boundary Conditions
• 12.3.5. ∙ The Robin Boundary Condition for the Diffusion Equation
• 12.4. Source Terms and Duhamel’s Principle for the Diffusion and Wave Equations
• 12.5. ∙ Laplace’s Equations in a Rectangle and a Disk
• 12.5.1. ∙ Rectangle
• 12.5.2. ∙ The Disk
• 12.6. ∙ Extensions and Generalizations of the Separation of Variables Algorithm
• 12.7. ∙ Extensions, I: Multidimensional Classical Fourier Series: Solving the Diffusion Equation on a Rectangle
• 12.8. ∙ Extensions, II: Polar and Cylindrical Coordinates and Bessel Functions
• 12.8.1. ∙ Vibrations of a Drum and Bessel Functions
• 12.9. Extensions, III: Spherical Coordinates, Legendre Polynomials, Spherical Harmonics, and Spherical Bessel Functions
• 12.9.1. Separation of Variables for the 3D Laplace Equation in Spherical Coordinates
• 12.9.2. Legendre Polynomials and Associated Legendre Polynomials
• 12.9.3. Spherical Harmonics
• 12.9.4. Solving the 3D Diffusion Equation on the Ball
• 12.10. Extensions, IV: General Sturm-Liouville Problems
• 12.10.1. Regular Sturm-Liouville Problems
• 12.10.2. Singular Sturm-Liouville Problems
• 12.11. Separation of Variables for the Schrödinger Equation: Energy Levels of the Hydrogen Atom
• 12.12. Chapter Summary
• Exercises
• Chapter 13. Uniting the Big Three Second-Order Linear Equations, and What’s Next
• 13.1. Are There Other Important Linear Second-Order Partial Differential Equations? The Standard Classification
• 13.1.1. Classification of Linear Second-Order Partial Differential Equations
• 13.2. Reflection on Fundamental Solutions, Green’s Functions, Duhamel’s Principle, and the Role/Position of the Delta Function
• 13.2.1. Fundamental Solutions/Green’s Functions for the Laplacian
• 13.2.2. Fundamental Solutions/Green’s Functions of the Diffusion Equation
• 13.2.3. Fundamental Solutions/Green’s Functions of the 1D Wave Equation
• 13.2.4. Fundamental Solutions/Green’s Functions of the 3D Wave Equation
• Exercises
• 13.3. What’s Next? Towards a Future Volume on This Subject
• Appendix. Objects and Tools of Advanced Calculus
• A.1. Sets, Domains, and Boundaries in ℝ^{ℕ}
• A.2. Functions: Smoothness and Localization
• A.2.1. Function Classes Sorted by Smoothness
• A.2.2. Localization: Functions with Compact Support
• A.2.3. Boundary Values for Functions Defined on a Domain
• A.3. Gradient of a Function and Its Interpretations, Directional Derivatives, and the Normal Derivative
• A.3.1. The Fundamental Relationship Between the Gradient and Directional Derivatives
• A.3.2. Lagrange Multipliers: An Illuminating Illustration of the Meaning of the Gradient
• A.3.3. An Important Directional Derivative: The Normal Derivative on an Orientable Surface
• A.4. Integration
• A.4.1. Bulk, Surface, and Line Integrals
• A.4.2. Flux Integrals
• A.4.3. Improper Integrals, Singularities, and Integrability
• A.5. Evaluation and Manipulation of Integrals: Exploiting Radial Symmetry
• A.5.1. Spherical (Polar) Coordinates in ℝ³
• A.5.2. Integration of a Radially Symmetric Function
• A.5.3. Integration of General Functions over a Ball via Spherical Shells
• A.5.4. Rescalings and Translations
• A.6. Fundamental Theorems of Calculus: The Divergence Theorem, Integration by Parts, and Green’s First and Second Identities
• A.6.1. The Divergence Theorem
• A.6.2. Two Consequences of the Divergence Theorem: Green’s Theorem and a Componentwise Divergence Theorem
• A.6.3. A Match Made in Heaven: The Divergence + the Gradient = the Laplacian
• A.6.4. Integration by Parts and Green’s First and Second Identities
• A.7. Integral vs. Pointwise Results
• A.7.1. IPW (Integral to Pointwise) Theorems
• A.7.2. The Averaging Lemma
• A.8. Convergence of Functions and Convergence of Integrals
• A.9. Differentiation under the Integral Sign
• A.9.1. General Conditions for Legality
• A.9.2. Examples Where It Is Illegal
• A.9.3. A Leibnitz Rule
• A.10. Change in the Order of Integration
• A.10.1. The Fubini-Tonelli Theorem
• A.10.2. Examples Where It Is Illegal
• A.11. Thinking Dimensionally: Physical Variables Have Dimensions with Physical Units
• Exercises
• Bibliography
• Index

• Reviews

• This is really an excellent textbook. It covers a wealth of interesting material, it is written in a very clear and convincing style, and it explains ideas, rather than drowning the reader in technicalities, as many other books do. The author takes his task serious, by not restricting himself to just the presentation of definitions, theorems, and proofs, which makes reading often pretty dry, but also by giving some hints which should prevent unexperienced readers from walking into certain traps...

...Again, this is an excellent textbook. It addresses, according to the author, senior undergraduate students, but it is certainly of interest also for postgraduate or PhD students. If an undergraduate student is looking for a mathematical field with a beautiful theory, some surprising results, methods from other parts of mathematics, and a large variety of applications, a good 'test animal' is PDEs. So go ahead and buy this book, and you will believe me.

Jürgen Appell (Würzburg), zbMathOpen
• Overall, this is a clearly written and very thorough text, one that would support either classroom use or individual study for well-prepared students.

Bill Satzer, University of Minnesota, MAA Reviews
• Requests

Review Copy – for reviewers who would like to review an AMS book
Desk Copy – for instructors who have adopted an AMS textbook for a course
Examination Copy – for faculty considering an AMS textbook for a course
Permission – for use of book, eBook, or Journal content
Accessibility – to request an alternate format of an AMS title
Volume: 542022; 612 pp
MSC: Primary 35;

While partial differential equations (PDEs) are fundamental in mathematics and throughout the sciences, most undergraduate students are only exposed to PDEs through the method of separation of variations. This text is written for undergraduate students from different cohorts with one sole purpose: to facilitate a proficiency in many core concepts in PDEs while enhancing the intuition and appreciation of the subject. For mathematics students this will in turn provide a solid foundation for graduate study. A recurring theme is the role of concentration as captured by Dirac's delta function. This both guides the student into the structure of the solution to the diffusion equation and PDEs involving the Laplacian and invites them to develop a cognizance for the theory of distributions. Both distributions and the Fourier transform are given full treatment.

The book is rich with physical motivations and interpretations, and it takes special care to clearly explain all the technical mathematical arguments, often with pre-motivations and post-reflections. Through these arguments the reader will develop a deeper proficiency and understanding of advanced calculus. While the text is comprehensive, the material is divided into short sections, allowing particular issues/topics to be addressed in a concise fashion. Sections which are more fundamental to the text are highlighted, allowing the instructor several alternative learning paths. The author's unique pedagogical style also makes the text ideal for self-learning.

Undergraduate and graduate students interested in partial differential equations.

• Preface
• 0.1. Who Should Take a First Course in PDEs?
• 0.2. A Text for All Three Groups: Grounding in Core Concepts and Topics
• 0.3. Basic Structure of the Text
• 0.3.1. Presentation and Modularization of Material
• 0.3.2. Choice for the Orderings of Chapters
• 0.3.3. Codependence and Different Orderings of Chapters
• 0.4. Prerequisites
• 0.4.1. Advanced Calculus and the Appendix
• 0.4.2. Breadth and Nonrigidity
• 0.5. Acknowledgments
• Chapter 1. Basic Definitions
• 1.1. ∙ Notation
• 1.2. ∙ What Are Partial Differential Equations and Why Are They Ubiquitous?
• 1.3. ∙ What Exactly Do We Mean by a Solution to a PDE?
• 1.4. ∙ Order, Linear vs. Nonlinear, Scalar vs. Systems
• 1.4.1. ∙ Order
• 1.4.2. ∙ Linear vs. Nonlinear
• 1.4.3. ∙ The Principle of Superposition for Linear PDEs
• 1.4.4. ∙ Scalar vs. Systems
• 1.5. ∙ General Solutions, Arbitrary Functions, Auxiliary Conditions, and the Notion of a Well-Posed Problem
• 1.5.1. ∙ General Solutions and Arbitrary Functions
• 1.5.2. ∙ Auxiliary Conditions: Boundary and Initial Conditions
• 1.5.3. ∙ General Auxiliary Conditions and the Cauchy Problem
• 1.5.4. ∙ A Well-Posed Problem
• 1.6. ∙ Common Approaches and Themes in Solving PDEs
• 1.6.1. ∙ Assuming We Have a Solution and Going Forward
• 1.6.2. ∙ Explicit vs. Nonexplicit Solutions, No Solutions, and Approximate Solutions
• Exercises
• Chapter 2. First-Order PDEs and the Method of Characteristics
• 2.1. ∙ Prelude: A Few Simple Examples Illustrating the Notion and Geometry of Characteristics
• 2.2. ∙ The Method of Characteristics, Part I: Linear Equations
• 2.2.1. ∙ A Few Examples
• 2.2.2. ∙ Temporal Equations: Using Time to Parametrize the Characteristics
• 2.2.3. ∙ More Than Two Independent Variables
• 2.2.4. ∙ Transport Equations in Three Space Dimensions with Constant Velocity
• 2.2.5. Transport Equations in Three Space Dimensions with Space Varying Velocity
• 2.2.6. The Continuity Equation in Three Space Dimensions: A Derivation
• 2.2.7. Semilinear Equations
• 2.2.8. Noncharacteristic Data and the Transversality Condition
• 2.3. ∙ An Important Quasilinear Example: The Inviscid Burgers Equation
• 2.4. ∙ The Method of Characteristics, Part II: Quasilinear Equations
• 2.4.1. ∙ A Few Examples
• 2.4.2. ∙ More Than Two Independent Variables
• 2.4.3. Pausing to Reflect on the Inherent Logic Behind the Method of Characteristics and Local Solutions of Quasilinear Equations
• 2.5. The Method of Characteristics, Part III: General First-Order Equations
• 2.5.1. The Notation
• 2.5.2. The Characteristic Equations
• 2.5.3. Linear and Quasilinear Equations in 𝑁 independent variables
• 2.5.4. Two Fully Nonlinear Examples
• 2.5.5. The Eikonal Equation
• 2.5.6. Hamilton-Jacobi Equations
• 2.5.7. The Level Set Equation and Interface Motion
• 2.6. ∙ Some General Questions
• 2.7. ∙ A Touch of Numerics, I: Computing the Solution of the Transport Equation
• 2.7.1. ∙ Three Consistent Schemes
• 2.7.2. ∙ von Neumann Stability Analysis
• 2.8. The Euler Equations: A Derivation
• 2.8.1. Conservation of Mass and the Continuity Equation
• 2.8.2. Conservation of Linear Momentum and Pressure
• 2.8.3. Gas Dynamics: The Compressible Euler Equations
• 2.8.4. An Ideal Liquid: The Incompressible Euler Equations
• 2.8.5. A Viscous Liquid: The Navier-Stokes Equations
• 2.8.6. Spatial vs. Material Coordinates and the Material Time Derivative
• 2.9. Chapter Summary
• Exercises
• Chapter 3. The Wave Equation in One Space Dimension
• 3.1. ∙ Derivation: A Vibrating String
• 3.2. ∙ The General Solution of the 1D Wave Equation
• 3.3. ∙ The Initial Value Problem and Its Explicit Solution: D’Alembert’s Formula
• 3.4. ∙ Consequences of D’Alembert’s Formula: Causality
• 3.4.1. ∙ The Domain of Dependence and Influence
• 3.4.2. ∙ Two Examples: A Plucked String and a Hammer Blow
• 3.5. ∙ Conservation of the Total Energy
• 3.6. ∙ Sources
• 3.6.1. ∙ Duhamel’s Principle
• 3.6.2. Derivation via Green’s Theorem
• 3.7. ∙ Well-Posedness of the Initial Value Problem and Time Reversibility
• 3.8. ∙ The Wave Equation on the Half-Line with a Fixed Boundary: Reflections
• 3.8.1. ∙ A Dirichlet (Fixed End) Boundary Condition and Odd Reflections
• 3.8.2. ∙ Causality with Respect to the Fixed Boundary
• 3.8.3. ∙ The Plucked String and Hammer Blow Examples with a Fixed Left End
• 3.9. ∙ Neumann and Robin Boundary Conditions
• 3.10. ∙ Finite String Boundary Value Problems
• 3.11. ∙ A Touch of Numerics, II: Numerical Solution to the Wave Equation
• 3.12. Some Final Remarks
• 3.12.1. Nonsmooth Solutions
• 3.12.2. Heterogeneous Media and Scattering
• 3.12.3. Finite Propagation Speed, Other “Wave” Equations, and Dispersion
• 3.12.4. Characterizing Dispersion in PDEs via the Ubiquitous Traveling Wave Solution
• 3.13. Chapter Summary
• Exercises
• Chapter 4. The Wave Equation in Three and Two Space Dimensions
• 4.1. ∙ Two Derivations of the 3D Wave Equation
• 4.1.1. ∙ Derivation 1: Electromagnetic Waves from Maxwell’s Equations
• 4.1.2. Derivation 2: Acoustics from the Euler Equations
• 4.2. ∙ Three Space Dimensions: The Initial Value Problem and Its Explicit Solution
• 4.2.1. ∙ Kirchhoff’s Formula
• 4.2.2. ∙ Consequences of Kirchhoff’s Formula: Causality and the Huygens Principle
• 4.2.3. ∙ Kirchhoff’s Formula via Spherical Means
• 4.2.4. Full Details: The Proof of Kirchhoff’s Formula
• 4.3. Two Space Dimensions: The 2D Wave Equation and Its Explicit Solution
• 4.3.1. The Solution via the Method of Descent
• 4.3.2. Causality in 2D
• 4.4. Some Final Remarks and Geometric Optics
• 4.4.1. The Wave Equation in Space Dimensions Larger Than Three
• 4.4.2. Regularity
• 4.4.3. Nonconstant Coefficients and Inverse Problems
• 4.4.4. Geometric Optics and the Eikonal Equation
• 4.5. Chapter Summary
• Exercises
• Chapter 5. The Delta “Function” and Distributions in One Space Dimension
• 5.1. ∙ Real-Valued Functions
• 5.1.1. ∙ What Is a Function?
• 5.1.2. ∙ Why Integrals (or Averages) of a Function Trump Pointwise Values
• 5.1.3. ∙ Singularities of Functions from the Point of View of Averages
• 5.2. ∙ The Delta “Function” and Why It Is Not a Function. Motivation for Generalizing the Notion of a Function
• 5.2.1. ∙ The Delta “Function” and the Derivative of the Heaviside Function
• 5.2.2. ∙ The Delta “Function” as a Limit of a Sequence of Functions Which Concentrate
• 5.3. ∙ Distributions (Generalized Functions)
• 5.3.1. ∙ The Class of Test Functions 𝐶_{𝑐}^{∞}(ℝ)
• 5.3.2. ∙ The Definition of a Distribution
• 5.3.3. ∙ Functions as Distributions
• 5.3.4. ∙ The Precise Definition of the Delta “Function” as a Distribution
• 5.4. ∙ Derivative of a Distribution
• 5.4.1. ∙ Motivation via Integration by Parts of Differentiable Functions
• 5.4.2. ∙ The Definition of the Derivative of a Distribution
• 5.4.3. ∙ Examples of Derivatives of Piecewise Smooth Functions in the Sense of Distributions
• 5.5. ∙ Convergence in the Sense of Distributions
• 5.5.1. ∙ The Definition
• 5.5.2. ∙ Comparisons of Distributional versus Pointwise Convergence of Functions
• 5.5.3. ∙ The Distributional Convergence of a Sequence of Functions to the Delta Function: Four Examples
• 5.5.4. 𝜀 vs. 𝑁 Proofs for the Sequences (5.23) and (5.24)
• 5.5.5. ∙ The Distributional Convergence of sin𝑛𝑥
• 5.5.6. ∙ The Distributional Convergence of the Sinc Functions and the Dirichlet Kernel: Two Sequences Directly Related to Fourier Analysis
• 5.6. Dirac’s Intuition: Algebraic Manipulations with the Delta Function
• 5.6.1. Rescaling and Composition with Polynomials
• 5.6.2. Products of Delta Functions in Different Variables
• 5.6.3. Symmetry in the Argument
• 5.7. ∙ Distributions Defined on an Open Interval and Larger Classes of Test Functions
• 5.7.1. ∙ Distributions Defined over a Domain
• 5.7.2. ∙ Larger Classes of Test Functions
• 5.8. Nonlocally Integrable Functions as Distributions: The Distribution PV 1/𝑥
• 5.8.1. Three Distributions Associated with the Function 1/𝑥
• 5.8.2. A Second Way to Write 𝑃𝑉1/𝑥
• 5.8.3. A Third Way to Write 𝑃𝑉1/𝑥
• 5.8.4. The Distributional Derivative of 𝑃𝑉1/𝑥
• 5.9. Chapter Summary
• Exercises
• Chapter 6. The Fourier Transform
• 6.1. ∙ Complex Numbers
• 6.2. ∙ Definition of the Fourier Transform and Its Fundamental Properties
• 6.2.1. ∙ The Definition
• 6.2.2. ∙ Differentiation and the Fourier Transform
• 6.2.3. ∙ Fourier Inversion via the Delta Function
• 6.2.4. Finding the Fourier and Inverse Fourier Transforms of Particular Functions
• 6.2.5. The Fourier Transform of a Complex-Valued Function
• 6.3. ∙ Convolution of Functions and the Fourier Transform
• 6.3.1. ∙ The Definition of Convolution
• 6.3.2. ∙ Differentiation of Convolutions
• 6.3.3. ∙ Convolution and the Fourier Transform
• 6.3.4. Convolution as a Way of Smoothing Out Functions and Generating Test Functions in 𝐶^{∞}_{𝑐}
• 6.4. ∙ Other Important Properties of the Fourier Transform
• 6.5. Duality: Decay at Infinity vs. Smoothness
• 6.6. Plancherel’s Theorem and the Riemann-Lebesgue Lemma
• 6.6.1. The Spaces 𝐿¹(ℝ) and 𝕃²(ℝ)
• 6.6.2. Extending the Fourier Transform to Square-Integrable Functions and Plancherel’s Theorem
• 6.6.3. The Riemann-Lebesgue Lemma
• 6.7. The 2𝜋 Issue and Other Possible Definitions of the Fourier Transform
• 6.8. ∙ Using the Fourier Transform to Solve Linear PDEs, I: The Diffusion Equation
• 6.8.1. ∙ Prelude: Using the Fourier Transform to Solve a Linear ODE
• 6.8.2. ∙ Using the Fourier Transform to Solve the Diffusion Equation
• 6.9. The Fourier Transform of a Tempered Distribution
• 6.9.1. Can We Extend the Fourier Transform to Distributions?
• 6.9.2. The Schwartz Class of Test Functions and Tempered Distributions
• 6.9.3. The Fourier Transform of 𝑓(𝑥)≡1 and the Delta Function
• 6.9.4. The Fourier Transform of 𝑓(𝑥)=𝑒^{𝑖𝑎𝑥} and the Delta Function 𝛿ₐ
• 6.9.5. The Fourier Transform of Sine, Cosine, and Sums of Delta Functions
• 6.9.6. The Fourier Transform of 𝑥
• 6.9.7. The Fourier Transform of the Heaviside and Sgn Functions and PV 1/𝑥
• 6.9.8. Convolution of a Tempered Distribution and a Function
• 6.10. Using the Fourier Transform to Solve PDEs, II: The Wave Equation
• 6.11. The Fourier Transform in Higher Space Dimensions
• 6.11.1. Definition and Analogous Properties
• 6.11.2. The Fourier Transform of a Radially Symmetric Function and Bessel Functions
• 6.12. Frequency, Harmonics, and the Physical Meaning of the Fourier Transform
• 6.12.1. Plane Waves in One Dimension
• 6.12.2. Interpreting the Fourier Inversion Formula
• 6.12.3. Revisiting the Wave Equation
• 6.12.4. Properties of the Fourier Transform, Revisited
• 6.12.5. The Uncertainty Principle
• 6.13. A Few Words on Other Transforms
• 6.14. Chapter Summary
• 6.15. Summary Tables
• Exercises
• Chapter 7. The Diffusion Equation
• 7.1. ∙ Derivation 1: Fourier’s/Fick’s Law
• 7.2. ∙ Solution in One Space Dimension and Properties
• 7.2.1. ∙ The Fundamental Solution/Heat Kernel and Its Properties
• 7.2.2. ∙ Properties of the Solution Formula
• 7.2.3. The Proof for the Solution to the Initial Value Problem: The Initial Conditions and the Delta Function
• 7.3. ∙ Derivation 2: Limit of Random Walks
• 7.3.1. ∙ Numerical Approximation of Second Derivatives
• 7.3.2. ∙ Random Walks
• 7.3.3. ∙ The Fundamental Limit and the Diffusion Equation
• 7.3.4. ∙ The Limiting Dynamics: Brownian Motion
• 7.4. Solution via the Central Limit Theorem
• 7.4.1. Random Variables, Probability Densities and Distributions, and the Normal Distribution
• 7.4.2. The Central Limit Theorem
• 7.4.3. Application to Our Limit of Random Walks and the Solution to the Diffusion Equation
• 7.5. ∙ Well-Posedness of the IVP and Ill-Posedness of the Backward Diffusion Equation
• 7.5.1. ∙ Nonuniqueness of the IVP of the Diffusion Equation
• 7.5.2. ∙ Ill-Posedness of the Backward Diffusion Equation
• 7.5.3. Deblurring in Image Processing
• 7.6. ∙ Some Boundary Value Problems in the Context of Heat Flow
• 7.6.1. ∙ Dirichlet and Neumann Boundary Conditions
• 7.6.2. ∙ The Robin Condition and Heat Transfer
• 7.7. ∙ The Maximum Principle on a Finite Interval
• 7.8. Source Terms and Duhamel’s Principle Revisited
• 7.8.1. An Intuitive and Physical Explanation of Duhamel’s Principle for Heat Flow with a Source
• 7.9. The Diffusion Equation in Higher Space Dimensions
• 7.10. ∙ A Touch of Numerics, III: Numerical Solution to the Diffusion Equation
• 7.11. Addendum: The Schrödinger Equation
• 7.12. Chapter Summary
• Exercises
• Chapter 8. The Laplacian, Laplace’s Equation, and Harmonic Functions
• 8.1. ∙ The Dirichlet and Neumann Boundary Value Problems for Laplace’s and Poisson’s Equations
• 8.2. ∙ Derivation and Physical Interpretations 1: Concentrations in Equilibrium
• 8.3. Derivation and Physical Interpretations 2: The Dirichlet Problem and Poisson’s Equation via 2D Random Walks/Brownian Motion
• 8.4. ∙ Basic Properties of Harmonic Functions
• 8.4.1. ∙ The Mean Value Property
• 8.4.2. ∙ The Maximum Principle
• 8.4.3. ∙ The Dirichlet Principle
• 8.4.4. Smoothness (Regularity)
• 8.5. ∙ Rotational Invariance and the Fundamental Solution
• 8.6. ∙ The Discrete Form of Laplace’s Equation
• 8.7. The Eigenfunctions and Eigenvalues of the Laplacian
• 8.7.1. Eigenvalues and Energy: The Rayleigh Quotient
• 8.8. The Laplacian and Curvature
• 8.8.1. Principal Curvatures of a Surface
• 8.8.2. Mean Curvature
• 8.8.3. Curvature and Invariance
• 8.9. Chapter Summary
• Exercises
• Chapter 9. Distributions in Higher Dimensions and Partial Differentiation in the Sense of Distributions
• 9.1. ∙ The Test Functions and the Definition of a Distribution
• 9.2. ∙ Convergence in the Sense of Distributions
• 9.3. ∙ Partial Differentiation in the Sense of Distributions
• 9.3.1. ∙ The Notation and Definition
• 9.3.2. ∙ A 2D Jump Discontinuity Example
• 9.4. ∙ The Divergence and Curl in the Sense of Distributions: Two Important Examples
• 9.4.1. ∙ The Divergence of the Gravitational Vector Field
• 9.4.2. The Curl of a Canonical Vector Field
• 9.5. ∙ The Laplacian in the Sense of Distributions and a Fundamental Example
• 9.6. Distributions Defined on a Domain (with and without Boundary)
• 9.7. Interpreting Many PDEs in the Sense of Distributions
• 9.7.1. Our First Example Revisited!
• 9.7.2. Burgers’s Equation and the Rankine-Hugoniot Jump Conditions
• 9.7.3. The Wave Equation with a Delta Function Source
• 9.7.4. Incorporating Initial Values into a Distributional Solution
• 9.7.5. Not All PDEs Can Be Interpreted in the Sense of Distributions
• 9.8. A View Towards Sobolev Spaces
• 9.9. Fourier Transform of an 𝑁-dimensional Tempered Distribution
• 9.10. Using the Fourier Transform to Solve Linear PDEs, III: Helmholtz and Poisson Equations in Three Space
• 9.11. Chapter Summary
• Exercises
• Chapter 10. The Fundamental Solution and Green’s Functions for the Laplacian
• 10.1. ∙ The Proof for the Distributional Laplacian of 1over |𝐱|
• 10.2. ∙ Unlocking the Power of the Fundamental Solution for the Laplacian
• 10.2.1. ∙ The Fundamental Solution Is Key to Solving Poisson’s Equation
• 10.2.2. ∙ The Fundamental Solution Gives a Representation Formula for Any Harmonic Function in Terms of Boundary Data
• 10.3. ∙ Green’s Functions for the Laplacian with Dirichlet Boundary Conditions
• 10.3.1. ∙ The Definition of the Green’s Function with Dirichlet Boundary Conditions
• 10.3.2. Using the Green’s Function to Solve the Dirichlet Problem for Laplace’s Equation
• 10.3.3. ∙ Uniqueness and Symmetry of the Green’s Function
• 10.3.4. ∙ The Fundamental Solution and Green’s Functions in One Space Dimension
• 10.4. ∙ Green’s Functions for the Half-Space and Ball in 3D
• 10.4.1. ∙ Green’s Function for the Half-Space
• 10.4.2. ∙ Green’s Function for the Ball
• 10.4.3. The Proof for Theorem 10.9
• 10.4.4. Green’s Functions for Other Domains and Differential Operators
• 10.5. Green’s Functions for the Laplacian with Neumann Boundary Conditions
• 10.5.1. Finding the Neumann Green’s Function for the Half-Space in ℝ³
• 10.5.2. Finding the Neumann Green’s Function for the Ball in ℝ³
• 10.6. A Physical Illustration in Electrostatics: Coulomb’s Law, Gauss’s Law, the Electric Field, and Electrostatic Potential
• 10.6.1. Coulomb’s Law and the Electrostatic Force
• 10.6.2. The Electrostatic Potential: The Fundamental Solution and Poisson’s Equation
• 10.6.3. Green’s Functions: Grounded Conducting Plates, Induced Charge Densities, and the Method of Images
• 10.6.4. Interpreting the Solution Formula for the Dirichlet Problem
• 10.7. Chapter Summary
• Exercises
• Chapter 11. Fourier Series
• 11.1. ∙ Prelude: The Classical Fourier Series —the Fourier Sine Series, the Fourier Cosine Series, and the Full Fourier Series
• 11.1.1. ∙ The Fourier Sine Series
• 11.1.2. ∙ The Fourier Cosine Series
• 11.1.3. ∙ The Full Fourier Series
• 11.1.4. ∙ Three Examples
• 11.1.5. ∙ Viewing the Three Fourier Series as Functions over ℝ
• 11.1.6. ∙ Convergence, Boundary Values, Piecewise Continuity, and Periodic Extensions
• 11.1.7. Complex Version of the Full Fourier Series
• 11.2. ∙ Why Cosines and Sines? Eigenfunctions, Eigenvalues, and Orthogonality
• 11.2.1. ∙ Finite Dimensions —the Linear Algebra of Vectors
• 11.2.2. ∙ Infinite Dimensions —the Linear Algebra of Functions
• 11.2.3. ∙ The Linear Operator 𝒜=-𝒹²over 𝒹𝓍² and Symmetric Boundary Conditions
• 11.3. ∙ Fourier Series in Terms of Eigenfunctions of 𝒜 with a Symmetric Boundary Condition
• 11.3.1. ∙ The Eigenfunctions and Respective Fourier Series Associated with the Four Standard Symmetric Boundary Conditions
• 11.3.2. ∙ The Miracle: These Sets of Eigenfunctions Span the Space of All Reasonable Functions
• 11.4. ∙ Convergence, I: The 𝐿² Theory, Bessel’s Inequality, and Parseval’s Equality
• 11.4.1. ∙ 𝐿² Convergence of a Sequence of Functions
• 11.4.2. ∙ 𝐿² Convergence of Fourier Series
• 11.4.3. ∙ Bessel’s Inequality and Reducing the 𝐿² Convergence Theorem to Parseval’s Equality
• 11.4.4. The Riemann-Lebesgue Lemma and an Application of Parseval’s Equality
• 11.5. ∙ Convergence, II: The Dirichlet Kernel and Pointwise Convergence of the Full Fourier Series
• 11.5.1. ∙ Pointwise Convergence of a Sequence of Functions
• 11.5.2. ∙ Pointwise Convergence of the Full Fourier Series: The Dirichlet Kernel and the Delta Function
• 11.5.3. ∙ The Proof of Pointwise Convergence of the Full Fourier Series
• 11.6. Term-by-Term Differentiation and Integration of Fourier Series
• 11.6.1. Term-by-Term Differentiation
• 11.6.2. Term-by-Term Integration
• 11.7. Convergence, III: Uniform Convergence
• 11.7.1. Uniform Convergence of Functions
• 11.7.2. A Criterion for the Uniform Convergence of Fourier Series
• 11.7.3. The Proof of Theorem 11.9
• 11.7.4. The Gibbs Phenomenon
• 11.8. What Is the Relationship Between Fourier Series and the Fourier Transform?
• 11.8.1. Sending 𝑙→∞ in the Full Fourier Series
• 11.8.2. Taking the Distributional Fourier Transform of a Periodic Function
• 11.9. Chapter Summary
• Exercises
• Chapter 12. The Separation of Variables Algorithm for Boundary Value Problems
• 12.1. ∙ The Basic Separation of Variables Algorithm
• 12.1.1. ∙ The Diffusion Equation with Homogeneous Dirichlet Boundary Conditions
• 12.1.2. ∙ The Diffusion Equation with Homogenous Neumann Boundary Conditions
• 12.2. ∙ The Wave Equation
• 12.2.1. ∙ The Wave Equation with Homogeneous Dirichlet Boundary Conditions
• 12.2.2. The Wave Equation with Homogeneous Neumann Boundary Conditions
• 12.3. ∙ Other Boundary Conditions
• 12.3.1. ∙ Inhomogeneous Dirichlet Boundary Conditions
• 12.3.2. ∙ Mixed Homogeneous Boundary Conditions
• 12.3.3. ∙ Mixed Inhomogeneous Boundary Conditions
• 12.3.4. ∙ Inhomogeneous Neumann Boundary Conditions
• 12.3.5. ∙ The Robin Boundary Condition for the Diffusion Equation
• 12.4. Source Terms and Duhamel’s Principle for the Diffusion and Wave Equations
• 12.5. ∙ Laplace’s Equations in a Rectangle and a Disk
• 12.5.1. ∙ Rectangle
• 12.5.2. ∙ The Disk
• 12.6. ∙ Extensions and Generalizations of the Separation of Variables Algorithm
• 12.7. ∙ Extensions, I: Multidimensional Classical Fourier Series: Solving the Diffusion Equation on a Rectangle
• 12.8. ∙ Extensions, II: Polar and Cylindrical Coordinates and Bessel Functions
• 12.8.1. ∙ Vibrations of a Drum and Bessel Functions
• 12.9. Extensions, III: Spherical Coordinates, Legendre Polynomials, Spherical Harmonics, and Spherical Bessel Functions
• 12.9.1. Separation of Variables for the 3D Laplace Equation in Spherical Coordinates
• 12.9.2. Legendre Polynomials and Associated Legendre Polynomials
• 12.9.3. Spherical Harmonics
• 12.9.4. Solving the 3D Diffusion Equation on the Ball
• 12.10. Extensions, IV: General Sturm-Liouville Problems
• 12.10.1. Regular Sturm-Liouville Problems
• 12.10.2. Singular Sturm-Liouville Problems
• 12.11. Separation of Variables for the Schrödinger Equation: Energy Levels of the Hydrogen Atom
• 12.12. Chapter Summary
• Exercises
• Chapter 13. Uniting the Big Three Second-Order Linear Equations, and What’s Next
• 13.1. Are There Other Important Linear Second-Order Partial Differential Equations? The Standard Classification
• 13.1.1. Classification of Linear Second-Order Partial Differential Equations
• 13.2. Reflection on Fundamental Solutions, Green’s Functions, Duhamel’s Principle, and the Role/Position of the Delta Function
• 13.2.1. Fundamental Solutions/Green’s Functions for the Laplacian
• 13.2.2. Fundamental Solutions/Green’s Functions of the Diffusion Equation
• 13.2.3. Fundamental Solutions/Green’s Functions of the 1D Wave Equation
• 13.2.4. Fundamental Solutions/Green’s Functions of the 3D Wave Equation
• Exercises
• 13.3. What’s Next? Towards a Future Volume on This Subject
• Appendix. Objects and Tools of Advanced Calculus
• A.1. Sets, Domains, and Boundaries in ℝ^{ℕ}
• A.2. Functions: Smoothness and Localization
• A.2.1. Function Classes Sorted by Smoothness
• A.2.2. Localization: Functions with Compact Support
• A.2.3. Boundary Values for Functions Defined on a Domain
• A.3. Gradient of a Function and Its Interpretations, Directional Derivatives, and the Normal Derivative
• A.3.1. The Fundamental Relationship Between the Gradient and Directional Derivatives
• A.3.2. Lagrange Multipliers: An Illuminating Illustration of the Meaning of the Gradient
• A.3.3. An Important Directional Derivative: The Normal Derivative on an Orientable Surface
• A.4. Integration
• A.4.1. Bulk, Surface, and Line Integrals
• A.4.2. Flux Integrals
• A.4.3. Improper Integrals, Singularities, and Integrability
• A.5. Evaluation and Manipulation of Integrals: Exploiting Radial Symmetry
• A.5.1. Spherical (Polar) Coordinates in ℝ³
• A.5.2. Integration of a Radially Symmetric Function
• A.5.3. Integration of General Functions over a Ball via Spherical Shells
• A.5.4. Rescalings and Translations
• A.6. Fundamental Theorems of Calculus: The Divergence Theorem, Integration by Parts, and Green’s First and Second Identities
• A.6.1. The Divergence Theorem
• A.6.2. Two Consequences of the Divergence Theorem: Green’s Theorem and a Componentwise Divergence Theorem
• A.6.3. A Match Made in Heaven: The Divergence + the Gradient = the Laplacian
• A.6.4. Integration by Parts and Green’s First and Second Identities
• A.7. Integral vs. Pointwise Results
• A.7.1. IPW (Integral to Pointwise) Theorems
• A.7.2. The Averaging Lemma
• A.8. Convergence of Functions and Convergence of Integrals
• A.9. Differentiation under the Integral Sign
• A.9.1. General Conditions for Legality
• A.9.2. Examples Where It Is Illegal
• A.9.3. A Leibnitz Rule
• A.10. Change in the Order of Integration
• A.10.1. The Fubini-Tonelli Theorem
• A.10.2. Examples Where It Is Illegal
• A.11. Thinking Dimensionally: Physical Variables Have Dimensions with Physical Units
• Exercises
• Bibliography
• Index
• This is really an excellent textbook. It covers a wealth of interesting material, it is written in a very clear and convincing style, and it explains ideas, rather than drowning the reader in technicalities, as many other books do. The author takes his task serious, by not restricting himself to just the presentation of definitions, theorems, and proofs, which makes reading often pretty dry, but also by giving some hints which should prevent unexperienced readers from walking into certain traps...

...Again, this is an excellent textbook. It addresses, according to the author, senior undergraduate students, but it is certainly of interest also for postgraduate or PhD students. If an undergraduate student is looking for a mathematical field with a beautiful theory, some surprising results, methods from other parts of mathematics, and a large variety of applications, a good 'test animal' is PDEs. So go ahead and buy this book, and you will believe me.

Jürgen Appell (Würzburg), zbMathOpen
• Overall, this is a clearly written and very thorough text, one that would support either classroom use or individual study for well-prepared students.

Bill Satzer, University of Minnesota, MAA Reviews
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