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🔢 Complex Functions and Special Functions

Complex Functions and Special Functions for Materials Informatics

📚 5 Chapters 💻 35 Code Examples ⏱️ 90-110 minutes 📊 Intermediate
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🎯 Series Overview

Complex analysis forms the mathematical foundation for wave phenomena, heat conduction, and quantum mechanics in materials science. This series covers theory and implementation (Python/NumPy/SciPy) together, from complex function theory to residue theorem, Fourier transform, Laplace transform, Gamma function, Bessel function, and Legendre polynomials.

Learning Path

flowchart LR A[Chapter 1
Complex Numbers] B[Chapter 2
Analytic Functions] C[Chapter 3
Residue Theorem] D[Chapter 4
Fourier/Laplace Transforms] E[Chapter 5
Special Functions] 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 differentiation and integration of complex functions and apply Cauchy-Riemann relations
  • Calculate complex integrals using residue theorem
  • Understand theory and implement numerical computation of Fourier and Laplace transforms
  • Understand properties of Gamma function, Bessel function, and Legendre polynomials and apply them to materials science
  • Perform numerical computation and visualization of special functions using SciPy

📖 Prerequisites

You can learn with basics of calculus (complex numbers, differentiation and integration). It is desirable to understand basic Python usage.

Chapter 1
Complex Numbers and Complex Plane

Learn basic operations of complex numbers, polar form representation on complex plane, Euler's formula, and implement visualization techniques for complex functions. Understand geometric meaning of complex numbers and introduce applications to complex impedance and crystal structure analysis in materials science.

Four arithmetic operations of complex numbers Polar form and Euler's formula Visualization on complex plane Powers and roots of complex numbers NumPy implementation
💻 7 Code Examples ⏱️ 18-22 minutes
Read Chapter 1 →
Chapter 2
Analytic Functions and Complex Calculus

Learn differentiability of complex functions, Cauchy-Riemann relations, properties of analytic functions. Implement calculation methods of complex integrals and Cauchy's integral theorem, and cover applications to potential theory and fluid dynamics.

Complex differentiation Cauchy-Riemann equations Determination of analytic functions Complex integration Cauchy's integral theorem Conformal mapping
💻 7 Code Examples ⏱️ 18-22 minutes
Read Chapter 2 →
Chapter 3
Complex Integration and Residue Theorem

Learn Taylor series expansion and Laurent series expansion, classification of singularities (removable singularity, pole, essential singularity). Implement residue calculation methods and residue theorem, and cover evaluation of real integrals and applications to physics problems.

Taylor expansion Laurent expansion Classification of singularities Residue calculation Residue theorem Evaluation of real integrals SymPy implementation
💻 7 Code Examples ⏱️ 18-22 minutes
Read Chapter 3 →
Chapter 4
Fourier Transform and Laplace Transform

Learn theory and numerical implementation of Fourier series, Fourier transform, and Laplace transform. Cover frequency analysis, convolution theorem, filtering, and solving differential equations, and implement applications to signal processing and spectral analysis.

Fourier series Fourier transform FFT algorithm Laplace transform Convolution theorem Signal processing
💻 7 Code Examples ⏱️ 18-22 minutes
Read Chapter 4 →
Chapter 5
Special Functions and Orthogonal Polynomials

Learn special functions such as Gamma function, Bessel function, Legendre polynomials, and Hermite polynomials. Implement solving differential equations in cylindrical and spherical coordinate systems, applications to boundary value problems, properties and numerical computation of orthogonal polynomials.

Gamma function Bessel function Legendre polynomials Hermite polynomials Orthogonal polynomials Boundary value problems SciPy implementation
💻 7 Code Examples ⏱️ 18-22 minutes
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

Deep Dive Learning

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