Alternatives and similar repositories for PGNN:

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

• yuan-yin / CoDA
  Generalizing to New Physical Systems via Context-Informed Dynamics Model
  ☆24Updated 11 months ago
• Jianxun-Wang / PIMBRL
  Physics-informed Dyna-style model-based deep reinforcement learning for dynamic control
  ☆54Updated 2 years ago
• yuan-yin / APHYNITY
  Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting
  ☆45Updated last year
• sungyongs / dpgn
  Differentiable Physics-informed Graph Networks
  ☆65Updated 5 years ago
• ASK-Berkeley / physics-NNs-hard-constraints
  [ICLR 2024] Scaling physics-informed hard constraints with mixture-of-experts.
  ☆32Updated 9 months ago
• jaggbow / magnet
  This repository contains code for the paper "MAgNet: Mesh-Agnostic Neural PDE Solver" https://arxiv.org/abs/2210.05495
  ☆37Updated last year
• AI4Science-WestlakeU / le-pde-uq
  [AAAI24] LE-PDE-UQ endows deep learning-based surrogate models with robust and efficient uncertainty quantification capabilities for both…
  ☆16Updated last year
• shib0li / DNN-MFBO
  Multi-fidelity Bayesian Optimization via Deep Neural Nets
  ☆30Updated 4 years ago
• sisl / variational_koopman
  Source code for "Deep Variational Koopman Models: Inferring Koopman Observations for Uncertainty-Aware Dynamics Modeling and Control" fro…
  ☆39Updated 5 years ago
• erichson / koopmanAE
  Consistent Koopman Autoencoders
  ☆73Updated last year
• crispitagorico / torchspde
  Neural Stochastic PDEs: resolution-invariant modelling of continuous spatiotemporal dynamics
  ☆51Updated 2 years ago
• isds-neu / PiSL
  A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery.
  ☆25Updated 3 years ago
• DesmondZhong / Lagrangian_caVAE
  Official implementation for our paper "Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control"
  ☆18Updated 2 years ago
• menggedu / DISCOVER
  Deep identification of symbolic open-form PDEs via enhanced reinforcement-learning
  ☆34Updated 4 months ago
• NREL / phygnn
  physics-guided neural networks (phygnn)
  ☆91Updated this week
• csjiezhao / Physics-Based-Deep-Learning
  ☆23Updated 2 years ago
• yuan-yin / LEADS
  Learning Dynamical Systems that Generalize Across Environments
  ☆20Updated 3 years ago
• Rose-STL-Lab / AutoODE-DSL
  ☆13Updated 4 years ago
• gduthe / windfarm-gnn
  GNNs to predict wind farm-wide power, local flow variables and damage-equivalent loads.
  ☆16Updated 5 months ago
• gautica / meshgraphnet_pytorch
  ☆15Updated 2 years ago
• AI4Science-WestlakeU / diffphycon
  [NeurIPS2024] DiffPhyCon uses generative models to control complex physical systems
  ☆34Updated last month
• wuwushrek / physics_constrained_nn
  Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling
  ☆48Updated 3 years ago
• arjhuang / pise
  Physics Informed Deep Learning for Traffic State Estimation: Illustrations with LWR and CTM Models
  ☆15Updated last year
• sjunhongshen / UnifiedPDESolvers
  ☆19Updated 10 months ago
• Junyoungpark / CGS
  The official implementation of Convergent Graph Solvers (CGS)
  ☆21Updated 3 years ago
• robinhenry / gym-anm-exp
  Run MPC-based policies and train RL agents in gym-anm environments using implementations from Stable Baselines 3.
  ☆12Updated 2 years ago
• thuml / HelmFluid
  About code release of "HelmFluid: Learning Helmholtz Dynamics for Interpretable Fluid Prediction", ICML 2024. https://arxiv.org/pdf/2310.…
  ☆18Updated 9 months ago
• Rose-STL-Lab / Equivariant-Net
  ☆35Updated last year
• JRice15 / physics-informed-autoencoders
  Research project conducted at Pacific Northwest National Laboratory, exploring the use of physics-informed autoencoders to predict fluid …
  ☆35Updated 2 years ago
• deep-symbolic-mathematics / Multimodal-Symbolic-Regression
  [ICLR 2024 Spotlight] SNIP on Symbolic Regression: Deep Symbolic Regression with Multimodal Pretraining
  ☆17Updated 5 months ago