Index

NE 415 Notes

Navigation

Prologue
Lesson 1 - An Introduction to Python, Spyder, and Jupyter
Lesson 2 - Basic Operators and Common, Built-In Functions
Lesson 3 - One-Dimensional Data Processing with NumPy and Matplotlib
Lesson 4 - Two-Dimensional Data Processing with NumPy and Matplotlib
Lesson 5 - Monte Carlo First
Lesson 6 - Random Variables and Distributions
Lesson 7 - Sampling Discrete and Continuous Random Variables
Lesson 8 - Statistics and Estimators
Lesson 9 - Engineering the Simulation
Lesson 10 - From Balance Laws to Linear Systems
Lesson 11 - Matrix and Vector Operations
Lesson 12 - One Matrix, Many Scenarios
Lesson 13 - Symbolic Computation with SymPy
Lesson 14 - Norms, Sparse Matrices, and Iterative Methods
Lesson 15 - Orthogonality, Gram-Schmidt, and Projection
Lesson 16 - One Eigenpair from Iteration
Lesson 17 - The Whole Spectrum at Once
Lesson 18 - Eigenvalue Applications
Lesson 19 - Numerical Differentiation
Lesson 20 - Numerical Integration
Lesson 21 - Nonlinear Equations
Lesson 22 - Unconstrained Optimization
Lesson 23 - Constrained Optimization
Lesson 24 - Modeling Data with Least-Squares Fits
Lesson 25 - Modeling Data with Interpolation
Lesson 26 - Advection, Diffusion, and Reaction Models
Lesson 27 - Solving ODEs Symbolically
Lesson 28 - Solving IVPs with Euler's Method
Lesson 29 - Solving Systems of IVPs
Lesson 30 - Solving BVPs Numerically
Appendix A01 - Algorithms, Flowcharts, and Pseudocode
Appendix A02 - Conditional Statements and the Structure of Python Code
Appendix A03 - More on Conditionals
Appendix A04 - Loops in Python
Appendix A05 - More on Loops
Appendix A06 - Pythonic Containers
Appendix A07 - Processing Text Data
Appendix A08 - Basics of Functions
Appendix A09 - More on Functions
Appendix A10 - Recursive Functions
Appendix A11 - Algorithm Complexity and Numerical Experiments
Appendix A12 - Searching
Appendix A13 - Sorting
Appendix A14 - Modules and Packages
Appendix A15 - Unit Tests and Defensive Programming