csuastt / HardConstraintLinks
Official implementation of *A Unified Hard-Constraint Framework for Solving Geometrically Complex PDEs*
☆17Updated 2 years ago
Alternatives and similar repositories for HardConstraint
Users that are interested in HardConstraint are comparing it to the libraries listed below
Sorting:
- Generative turbulence model TurbDiff as proposed in "From Zero to Turbulence: Generative Modeling for 3D Flow Simulation", ICLR 2024☆29Updated this week
- LE-PDE accelerates PDEs' forward simulation and inverse optimization via latent global evolution, achieving significant speedup with SOTA…☆29Updated last year
- [ICLR 2024] Scaling physics-informed hard constraints with mixture-of-experts.☆35Updated last year
- Code for the ICLR 2020 paper "Learning to Control PDEs"☆35Updated 5 years ago
- Source code for paper "Learning the Solution Operator of Boundary Value Problems using Graph Neural Networks"☆20Updated last year
- Accelerating Physics Informed Neural Networks (PINNs) using Meshless Discretizations☆31Updated 2 years ago
- ☆15Updated 2 years ago
- Code repository for "Learned Turbulence Modelling with Differentiable Fluid Solvers"☆39Updated 3 years ago
- Simple demo on implementing data driven and physics informed Deep O Nets in pytorch☆19Updated last year
- This repository contains code, which was used to generate large-scale results in the HINTS paper.☆34Updated last year
- ☆37Updated 4 months ago
- [ICLR23] First deep learning-based surrogate model that jointly learns the evolution model and optimizes computational cost via remeshing☆41Updated last year
- Code for "Robust flow field reconstruction from limited measurements vis sparse representation" (J. Callaham, K. Maeda, and S. Brunton 20…☆14Updated 7 years ago
- Transfer learning on physics-informed neural networks for tracking the hemodynamics in the evolving false lumen of dissected aorta☆16Updated last year
- Coupled-Automatic-Numerical differentiation scheme for physics-informed neural network (can-PINN)☆32Updated 2 years ago
- To address some of the failure modes in training of physics informed neural networks, a Lagrangian architecture is designed to conform to…☆51Updated 2 years ago
- ☆13Updated 4 years ago
- Official implementation of the AIAA Journal paper "Uncertainty-aware Surrogate Models for Airfoil Flow Simulations with Denoising Diffusi…☆80Updated last year
- ☆10Updated 2 years ago
- The public repository about our joint FINN research project☆38Updated 3 years ago
- Synthetic Lagrangian Turbulence by Generative Diffusion Models☆26Updated last year
- ☆29Updated 3 years ago
- Neural SPH☆37Updated 2 weeks ago
- Codes for paper "Automatic Parameterization for Aerodynamic Shape Optimization via Deep Geometric Learning", published on AIAA Aviation F…☆20Updated 2 years ago
- ☆14Updated 4 years ago
- ☆24Updated 5 months ago
- The unsupervised learning problem trains a diffeomorphic spatio-temporal grid, that registers the output sequence of the PDEs onto a non-…☆19Updated 3 years ago
- ☆24Updated 2 years ago
- Physics-informed neural networks (PINNs)☆14Updated 3 years ago
- DON-LSTM: Multi-Resolution Learning with DeepONets and Long-Short Term Memory Neural Networks☆11Updated 3 months ago