Scientific Machine Learning (SciML) and Simulation

It is a repository to experiment Scientific Machine Learning (SciML) in simulating physical dynamics, understanding machine learning pros and cons in scientific computing, and discovering physical rules using the data-driven and physics-based method.

The foundamental crux of the project is to solve a variety of differential equations with machine learning.

The code has the following structure:

Physics

In Physics, it has the following experiments using SciML:

1. Pendulum
2. Spring Mass
3. Wave Propagation
4. Poisson
5. Lorenz

Games

In Games, it has

1. Hanoi Tower

Biology

In Biology, it contains

1. SEIR model for COVID-19

Utils

In Utils, it has

1. Symmetry Neural Network

SciML models include:

1. Physics Informed Neural Network (PINN)
2. Neural ODE
3. Universal Differential Equation
4. Hamitonian Neural Network