Applied Mathematics
7.4
Time Integration Methods
Scientific Programming
Preface
1
Installing python and setup
2
Acknowledgements
Programming fundamentals
3.1
Best practices
3.2
Basics
3.3
Functions
3.4
Data structures
3.5
Objects
3.6
File I/O
3.7
Debugging
3.8
Version control
NumPy/SciPy
4.1
NumPy Arrays
4.2
Linear Algebra
4.3
Optimization
4.4
Interpolation
4.5
Pandas
SymPy
5.1
Introduction
Matplotlib
6.1
Simple Plotting Examples
6.2
Interpolated Polygon Plots
Applied Mathematics
7.1
Introduction to Applied Mathematics
7.2
Numeric Differentiation
7.3
Numeric Integration
7.4
Time Integration Methods
7.5
Differential Equations
7.6
Root Finding Methods
7.7
Interpolation Methods
7.8
2D Interpolation - Bi-linear Shape Functions
7.9
Optimization Methods
Applications
8.1
Implementing FEM in python
8.2
Systematic Truss Analysis
8.3
Visualizing Truss Mesh
8.4
Parsing a mesh
8.5
Fusion360 API
8.6
Kinematics - Constraint Solving
8.7
Computational Thinking
8.8
Real-time simulation of a pendulum
MechanicsKit API
9.1
MechanicsKit API Reference
9.2
LaTeX Display Functions
9.3
Mesh Class
9.4
OneArray Class
9.5
Plotting Functions
9.6
Colormap Utilities
Applied Mathematics
7.4
Time Integration Methods
7.4
Time Integration Methods
Euler methods.
Runge-Kutta methods.
7.3
Numeric Integration
7.5
Differential Equations