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
The package phlearn for modelling pseudo-Hamiltonian systems by pseudo-Hamiltonian neural networks (PHNN), for ODEs and PDEs
Learn Hamiltonian from Trajectory & Lagrangian Correspondence from in-out data
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 80 example dynamical systems in cpp format
Code for the paper "Sparse Symplectically Integrated Neural Networks"
The Structure and Interpretation of Classical Mechanics
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.
Renormalization for the break-up of invariant tori in Hamiltonian flows
Solutions to Mathematical Methods of Classical Mechanics by V.A.Arnold
n-body system integrator
Jerarquía de ecuaciones diferenciales asociadas a una ecuación de evolución bi-Hamiltoniana, con un ejemplo canónico para la ecuación de Korteweg-De Vries.
One-dimensional Vlasov-Poisson equation and its Hamiltonian fluid reductions
Plotted phase space trajectories for different mechanical systems using Python.
pyHamSys is a Python package for scientific computations involving Hamiltonian systems
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