Alternatives and similar repositories for lagrangian_nns:

Users that are interested in lagrangian_nns are comparing it to the libraries listed below

• greydanus / hamiltonian-nn
  Code for our paper "Hamiltonian Neural Networks"
  ☆441Updated 3 years ago
• boschresearch / torchphysics
  ☆423Updated last month
• kpchamp / SindyAutoencoders
  ☆344Updated 3 years ago
• titu1994 / tfdiffeq
  Tensorflow implementation of Ordinary Differential Equation Solvers with full GPU support
  ☆218Updated 4 years ago
• NeuroDiffGym / neurodiffeq
  A library for solving differential equations using neural networks based on PyTorch, used by multiple research groups around the world, i…
  ☆719Updated 6 months ago
• jacobjinkelly / easy-neural-ode
  Code for the paper "Learning Differential Equations that are Easy to Solve"
  ☆274Updated 3 years ago
• BethanyL / DeepKoopman
  neural networks to learn Koopman eigenfunctions
  ☆387Updated 10 months ago
• MilesCranmer / symbolic_deep_learning
  Code for "Discovering Symbolic Models from Deep Learning with Inductive Biases"
  ☆742Updated last year
• dynamicslab / pykoopman
  A package for computing data-driven approximations to the Koopman operator.
  ☆327Updated 2 months ago
• DiffEqML / torchdyn
  A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods
  ☆1,429Updated 8 months ago
• mfinzi / constrained-hamiltonian-neural-networks
  ☆102Updated 3 years ago
• zabaras / transformer-physx
  Transformers for modeling physical systems
  ☆132Updated last year
• PredictiveIntelligenceLab / jaxpi
  ☆251Updated 2 months ago
• pdebench / PDEBench
  PDEBench: An Extensive Benchmark for Scientific Machine Learning
  ☆822Updated this week
• AdamCobb / hamiltorch
  PyTorch-based library for Riemannian Manifold Hamiltonian Monte Carlo (RMHMC) and inference in Bayesian neural networks
  ☆430Updated 5 months ago
• analysiscenter / pydens
  PyDEns is a framework for solving Ordinary and Partial Differential Equations (ODEs & PDEs) using neural networks
  ☆303Updated 11 months ago
• msurtsukov / neural-ode
  Jupyter notebook with Pytorch implementation of Neural Ordinary Differential Equations
  ☆721Updated 11 months ago
• neuraloperator / graph-pde
  Using graph network to solve PDEs
  ☆370Updated last year
• maziarraissi / DeepHPMs
  Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations
  ☆275Updated 2 years ago
• google / jax-cfd
  Computational Fluid Dynamics in JAX
  ☆784Updated 5 months ago
• SJ001 / AI-Feynman
  ☆660Updated 6 months ago
• Zymrael / awesome-neural-ode
  A collection of resources regarding the interplay between differential equations, deep learning, dynamical systems, control and numerical…
  ☆1,384Updated 4 months ago
• pdearena / pdearena
  ☆239Updated 4 months ago
• patrick-kidger / diffrax
  Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
  ☆1,524Updated this week
• dso-org / deep-symbolic-optimization
  A deep learning framework for symbolic optimization.
  ☆616Updated 2 weeks ago
• snagcliffs / PDE-FIND
  ☆239Updated 2 years ago
• tum-pbs / PhiFlow
  A differentiable PDE solving framework for machine learning
  ☆1,540Updated this week
• dynamicslab / pysindy
  A package for the sparse identification of nonlinear dynamical systems from data
  ☆1,508Updated this week
• esa / torchquad
  Numerical integration in arbitrary dimensions on the GPU using PyTorch / TF / JAX
  ☆193Updated 2 months ago
• patrick-kidger / torchcde
  Differentiable controlled differential equation solvers for PyTorch with GPU support and memory-efficient adjoint backpropagation.
  ☆424Updated last year