Welcome to SciML’s documentation!

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

We studied the following physical phenomenons:

  1. Pendulum

  2. Spring Mass

  3. Wave Propagation

  4. Poisson

  5. Lorenz

with the following SciML models:

Physics Informed NN PINN

Link: https://nips.cc/

Neural ODE (NODE)

Univeral Differential Equations (UDE)

Hamiltonian Neural Network (HNN)

Hamiltonian fundamentals:

Link: http://www.scholarpedia.org/article/Hamiltonian_systems

Indices and tables