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

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

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

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

Code for the paper "Sparse Symplectically Integrated Neural Networks"

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

pyHamSys is a Python package for scientific computations involving Hamiltonian systems

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

Conjugation method in configuration space for invariant tori of Hamiltonian systems

Numerical results for deterministic dynamics of a system coupled to a finite and chaotic bath.

Flows: classical, Hamiltonian, from OCP and more

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

Python library for quantum systems and simulation

Numerical work related to Clock Space Hamiltonian Simulation

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