A collection of resources regarding the interplay between differential equations, deep learning, dynamical systems, control and numerical methods.
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Updated
Jan 10, 2024
A collection of resources regarding the interplay between differential equations, deep learning, dynamical systems, control and numerical methods.
Symplectic Recurrent Neural Networks
Sampling-based approach to analyse neural networks using TensorFlow
Port-Hamiltonian Approach to Neural Network Training
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
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"
The Structure and Interpretation of Classical Mechanics
Learn Hamiltonian from Trajectory & Lagrangian Correspondence from in-out data
One-dimensional Vlasov-Poisson equation and its Hamiltonian fluid reductions
Numerical results for deterministic dynamics of a system coupled to a finite and chaotic bath.
Conjugation method in configuration space for invariant tori of Hamiltonian systems
Renormalization for the break-up of invariant tori in Hamiltonian flows
Theoretical project regarding hamiltonian and lagrangian neural network
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.
pyHamSys is a Python package for scientific computations involving Hamiltonian systems
Numerical work related to Clock Space Hamiltonian Simulation
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