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🌊 Partial Differential Equations and Boundary Value Problems

Partial Differential Equations and Boundary Value Problems for Materials Science

📚 5 Chapters 💻 35 Code Examples ⏱️ 100-120 min 📊 Intermediate
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🎯 Series Overview

Partial differential equations (PDEs) are essential for the mathematical description of diffusion, heat conduction, wave propagation, and phase transformations in materials science. This series covers theory from the heat equation, wave equation, and Laplace equation to separation of variables, Fourier series expansion, Green's function method, and numerical methods for boundary value problems, learning both theory and implementation (Python/NumPy/SciPy) in pairs.

Learning Path

flowchart LR A[Chapter 1
Wave Equation] B[Chapter 2
Heat Equation] C[Chapter 3
Laplace Equation] D[Chapter 4
Variational Methods] E[Chapter 5
Finite Element Method] A --> B --> C --> D --> E style A fill:#667eea,stroke:#764ba2,stroke-width:2px,color:#fff style B fill:#667eea,stroke:#764ba2,stroke-width:2px,color:#fff style C fill:#667eea,stroke:#764ba2,stroke-width:2px,color:#fff style D fill:#667eea,stroke:#764ba2,stroke-width:2px,color:#fff style E fill:#667eea,stroke:#764ba2,stroke-width:2px,color:#fff

📋 Learning Objectives

  • Understand the physical meaning and mathematical properties of heat, wave, and Laplace equations
  • Analytically solve PDEs using separation of variables method
  • Understand and apply Fourier series expansion and Sturm-Liouville theory
  • Understand solution methods using Green's function and eigenfunction expansion
  • Numerically solve PDEs using finite difference methods and simulate materials processes

📖 Prerequisites

Knowledge of basic calculus and vector analysis (partial derivatives, multiple integrals) and linear algebra (eigenvalue problems) is sufficient for learning. Understanding of basic Python usage is desirable.

Chapter 1
Heat Equation and Diffusion Phenomena

Learn from fundamental theory of 1D and multidimensional heat conduction to solution methods for initial value and boundary value problems, and temperature distribution calculations in materials. Implement analytical and numerical solutions of the diffusion equation based on Fourier's law, with applications to heat treatment processes.

Heat Equation Diffusion Equation Fourier's Law Initial Value Problems Boundary Conditions
💻 7 Code Examples ⏱️ 20-24 min
Read Chapter 1 →
Chapter 2
Wave Equation and Oscillation Phenomena

Learn wave propagation and d'Alembert's solution, standing wave formation, and vibration analysis of materials. Starting from string vibration, understand wave energy conservation laws and effects of boundary conditions, and implement applications to ultrasonic testing.

Wave Equation d'Alembert Solution Standing Waves String Vibration Energy Conservation
💻 7 Code Examples ⏱️ 20-24 min
Read Chapter 2 →
Chapter 3
Laplace Equation and Potential Problems

Learn electrostatic potential, properties of harmonic functions, and the maximum principle. Implement solution methods for Dirichlet and Neumann problems, construction and application of Green's functions, and analysis of steady-state heat conduction problems.

Laplace Equation Harmonic Functions Dirichlet Problem Neumann Problem Green's Function
💻 7 Code Examples ⏱️ 20-24 min
Read Chapter 3 →
Chapter 4
Separation of Variables and Fourier Series

Learn separation of variables techniques, solution construction by Fourier series expansion, and Sturm-Liouville theory and eigenvalue problems. Implement practical solution methods including orthogonality of eigenfunctions, applications to boundary value problems, and convergence arguments.

Separation of Variables Fourier Series Sturm-Liouville Theory Eigenvalue Problems Orthogonality
💻 7 Code Examples ⏱️ 20-24 min
Read Chapter 4 →
Chapter 5
Numerical Methods for Boundary Value Problems

Learn fundamentals of finite difference methods, time evolution using Crank-Nicolson method, and stability and convergence analysis. Implement applications to materials process simulations (heat treatment, diffusion, phase transformation) and master practical solution methods.

Finite Difference Method Crank-Nicolson Method Stability Analysis Convergence Heat Treatment Simulation
💻 7 Code Examples ⏱️ 20-24 min
Read Chapter 5 →

📚 Recommended Learning Paths

Pattern 1: Beginner - Theory and Practice Balanced (5-7 days)

Pattern 2: Intermediate - Fast Track (3 days)

Pattern 3: Topic-Focused - Computational Skills (1 day)

🎯 Overall Learning Outcomes

Upon completing this series, you will achieve:

Knowledge Level

Practical Skills

Application Ability

🛠️ Technologies and Tools Used

Main Libraries

Development Environment

Recommended Tools

🚀 Next Steps

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