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.
Port-Hamiltonian Approach to Neural Network Training
Symplectic Recurrent Neural Networks
Sampling-based approach to analyse neural networks using TensorFlow
Code for the paper "Sparse Symplectically Integrated Neural Networks"
Learn Hamiltonian from Trajectory & Lagrangian Correspondence from in-out data
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 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
The package phlearn for modelling pseudo-Hamiltonian systems by pseudo-Hamiltonian neural networks (PHNN), for ODEs and PDEs
Solutions to Mathematical Methods of Classical Mechanics by V.A.Arnold
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.
One-dimensional Vlasov-Poisson equation and its Hamiltonian fluid reductions
pyHamSys is a Python package for scientific computations involving Hamiltonian systems
Plotted phase space trajectories for different mechanical systems using Python.
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