A collection of resources regarding the interplay between differential equations, deep learning, dynamical systems, control and numerical methods.
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Updated
Jun 6, 2024
A collection of resources regarding the interplay between differential equations, deep learning, dynamical systems, control and numerical methods.
Port-Hamiltonian Approach to Neural Network Training
Symplectic Recurrent Neural Networks
Sampling-based approach to analyse neural networks using TensorFlow
Dash App - simulation of double pendulum equations of motion
Flows: classical, Hamiltonian, from OCP and more
Symplectic integration of Hamiltonian systems. Zymplectic is a pre-compiled GUI and engine with 2D/3D-graphics bundled with more than 70 example dynamical systems in cpp format
Learn Hamiltonian from Trajectory & Lagrangian Correspondence from in-out data
The package phlearn for modelling pseudo-Hamiltonian systems by pseudo-Hamiltonian neural networks (PHNN), for ODEs and PDEs
Code for the paper "Sparse Symplectically Integrated Neural Networks"
Conjugation method in configuration space for invariant tori of Hamiltonian systems
The Structure and Interpretation of Classical Mechanics
Numerical results for deterministic dynamics of a system coupled to a finite and chaotic bath.
Solutions to Mathematical Methods of Classical Mechanics by V.A.Arnold
Renormalization for the break-up of invariant tori in Hamiltonian flows
n-body system integrator
Theoretical project regarding hamiltonian and lagrangian neural network
Python library for quantum systems and simulation
Numerical work related to Clock Space Hamiltonian Simulation
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