KindXiaoming / aipoincare

Related projects: ⓘ

• NBoulle / RationalNets
  Code for the paper "Rational neural networks", NeurIPS 2020
  ☆23Updated 3 years ago
• lorenzrichter / PDE-backward-solver
  ☆19Updated 2 years ago
• mfinzi / constrained-hamiltonian-neural-networks
  ☆100Updated 3 years ago
• brandstetter-johannes / LPSDA
  Repo to the paper "Lie Point Symmetry Data Augmentation for Neural PDE Solvers"
  ☆46Updated last year
• jpzxshi / sympnets
  ☆23Updated 3 years ago
• dimitra-maoutsa / odes_for_sdes
  Deterministic particle dynamics for simulating Fokker-Planck probability flows
  ☆23Updated last year
• zhengdao-chen / SRNN
  Symplectic Recurrent Neural Networks
  ☆27Updated last year
• facebookresearch / neural-conservation-law
  code for "Neural Conservation Laws A Divergence-Free Perspective".
  ☆35Updated last year
• martenlienen / finite-element-networks
  Reference implementation of Finite Element Networks as proposed in "Learning the Dynamics of Physical Systems from Sparse Observations wi…
  ☆66Updated 4 months ago
• crispitagorico / torchspde
  Neural Stochastic PDEs: resolution-invariant modelling of continuous spatiotemporal dynamics
  ☆44Updated last year
• RSMI-NE / RSMI-NE
  A Python package for efficient optimisation of real-space renormalization group transformations using Tensorflow.
  ☆27Updated 11 months ago
• peterparity / PDE-VAE-pytorch
  PDE-VAE: Variational Autoencoder for Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning
  ☆29Updated 2 years ago
• afqueiruga / StatefulOdeNets
  Refining continuous-in-depth neural networks
  ☆39Updated 2 years ago
• yifanc96 / NonLinPDEs-GPsolver
  Code for the paper: Solving and Learning Nonlinear PDEs with Gaussian Processes
  ☆31Updated 8 months ago
• peterparity / symder
  SymDer: Symbolic Derivative Approach to Discovering Sparse Interpretable Dynamics from Partial Observations
  ☆17Updated 2 years ago
• RehMoritz / vmc_pde
  Code for the paper "Variational Monte Carlo Approach to Partial Differential Equations with Neural Networks" (https://arxiv.org/abs/2206.…
  ☆9Updated 2 years ago
• frankhan91 / BSDEEigen
  ☆11Updated 3 years ago
• PhIMaL / temporal_normalizing_flows
  ☆11Updated 3 years ago
• tonyshardlow / PICSPDE
  Python codes for Introduction to Computational Stochastic PDE
  ☆35Updated last week
• marvinpfoertner / linpde-gp
  Code for the Paper "Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers"
  ☆21Updated 4 months ago
• luckystarufo / multiscale_HiTS
  ☆68Updated 4 years ago
• pooyasf / DGM
  Solving High Dimensional Partial Differential Equations with Deep Neural Networks
  ☆29Updated 2 years ago
• gregversteeg / esh_dynamics
  Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid Sampling
  ☆34Updated 2 years ago
• DiffEqML / diffeqml-research
  This repository contains code released by DiffEqML Research
  ☆83Updated 2 years ago
• cagatayyildiz / npde
  Nonparametric Differential Equation Modeling
  ☆50Updated 6 months ago
• gaurav71531 / mwt-operator
  Multiwavelets-based operator model
  ☆56Updated 2 years ago
• NBoulle / greenlearning
  Learning Green's functions of partial differential equations with deep learning.
  ☆62Updated 8 months ago
• astanziola / fourier-neural-operator-flax
  An unofficial implementation of the Fourier Neural Operator in Flax
  ☆16Updated 3 months ago
• dynamicslab / langevin-regression
  Code for "Nonlinear stochastic modeling with Langevin regression" J. L. Callaham, J.-C. Loiseau, G. Rigas, and S. L. Brunton
  ☆24Updated 2 years ago
• anshu957 / OrderChaosHNN
  Hamiltonian neural network implementation for Henon Heiles dynamical system learning mix of order and chaos
  ☆11Updated 9 months ago