A collection of resources regarding the interplay between differential equations, deep learning, dynamical systems, control and numerical methods.

Symplectic Recurrent Neural Networks

Port-Hamiltonian Approach to Neural Network Training

Sampling-based approach to analyse neural networks using TensorFlow

The package phlearn for modelling pseudo-Hamiltonian systems by pseudo-Hamiltonian neural networks (PHNN), for ODEs and PDEs

Symplectic integration of Hamiltonian systems. Zymplectic is a pre-compiled GUI and engine with 2D/3D-graphics bundled with more than 80 example dynamical systems in cpp format

Official implementation of the paper "Neural Hamilton: Can A.I. Understand Hamiltonian Mechanics?"

Code for the paper "Sparse Symplectically Integrated Neural Networks"

pyHamSys is a Python package for scientific computations involving Hamiltonian systems

The Structure and Interpretation of Classical Mechanics

Dash App - Simulation of double pendulum equations of motion

Learn Hamiltonian from Trajectory & Lagrangian Correspondence from in-out data

Solutions to Mathematical Methods of Classical Mechanics by V.A.Arnold

Flows: classical, Hamiltonian, from OCP and more

Python Library for Hamiltonian Markov-Chain Monte-Carlo in Computational Statistics (pyHaiCS).

One-dimensional Vlasov-Poisson equation and its Hamiltonian fluid reductions

Conjugation method in configuration space for invariant tori of Hamiltonian systems

Python library for quantum systems and simulation

Quantum Computing with IBM's Qiskit toolkit

Renormalization for the break-up of invariant tori in Hamiltonian flows