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?"

pyHamSys is a Python package for scientific computations involving Hamiltonian systems

Code for the paper "Sparse Symplectically Integrated Neural Networks"

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

The Structure and Interpretation of Classical Mechanics

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

Dash App - Simulation of double pendulum equations of motion

Quantum Computing with IBM's Qiskit toolkit

Physics-based computation at scale — Hamiltonian dynamics, spectral theory, and statistical mechanics powering optimization, drug discovery, genomics, molecular proof, and agentic commerce.

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

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

Flows: classical, Hamiltonian, from OCP and more

JAX composable symplectic integrators: multi-map, implicit midpoint and Tao