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

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

Numerical work related to Clock Space Hamiltonian Simulation