Characterizing possible failure modes in physics-informed neural networks.
☆150Nov 18, 2021Updated 4 years ago
Alternatives and similar repositories for characterizing-pinns-failure-modes
Users that are interested in characterizing-pinns-failure-modes are comparing it to the libraries listed below
Sorting:
- ☆111Oct 16, 2021Updated 4 years ago
- ☆512Apr 1, 2025Updated 11 months ago
- [ICLR 2024] Scaling physics-informed hard constraints with mixture-of-experts.☆39Jun 21, 2024Updated last year
- ☆37Jan 2, 2024Updated 2 years ago
- ☆391Dec 3, 2022Updated 3 years ago
- Multiwavelets-based operator model☆69Feb 16, 2022Updated 4 years ago
- Official implementation of Scalable Transformer for PDE surrogate modelling☆54Mar 23, 2024Updated last year
- ☆55Oct 9, 2022Updated 3 years ago
- Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning☆95Aug 17, 2023Updated 2 years ago
- ☆91Sep 2, 2024Updated last year
- Implementation of the paper "Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism" [AAAI-MLPS 2021]☆280Oct 12, 2021Updated 4 years ago
- ☆207Feb 16, 2024Updated 2 years ago
- POD-PINN code and manuscript☆58Nov 10, 2024Updated last year
- Must-read Papers on Physics-Informed Neural Networks.☆1,434Dec 8, 2023Updated 2 years ago
- About Code Release for "Solving High-Dimensional PDEs with Latent Spectral Models" (ICML 2023), https://arxiv.org/abs/2301.12664☆80Apr 10, 2025Updated 10 months ago
- Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data☆50Jul 23, 2020Updated 5 years ago
- ☆116Feb 5, 2025Updated last year
- Repo to the paper "Lie Point Symmetry Data Augmentation for Neural PDE Solvers"☆57May 23, 2023Updated 2 years ago
- PDEBench: An Extensive Benchmark for Scientific Machine Learning☆1,078Feb 24, 2026Updated last week
- ☆244Oct 14, 2021Updated 4 years ago
- Accelerating Physics Informed Neural Networks (PINNs) using Meshless Discretizations☆34Apr 15, 2023Updated 2 years ago
- ☆29Jul 8, 2024Updated last year
- [ICLR 2024] Neural Spectral Methods: Self-supervised learning in the spectral domain.☆48Jan 31, 2024Updated 2 years ago
- ☆293Sep 14, 2024Updated last year
- Numerical assessments of a nonintrusive surrogate model based on recurrent neural networks and proper orthogonal decomposition: Rayleigh …☆10Dec 2, 2022Updated 3 years ago
- Code for Characterizing Scaling and Transfer Learning Behavior of FNO in SciML☆53May 31, 2023Updated 2 years ago
- Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonli…☆246Feb 1, 2023Updated 3 years ago
- A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data☆361Jul 14, 2023Updated 2 years ago
- Practicum on Supervised Learning in Function Spaces☆35Feb 17, 2022Updated 4 years ago
- ☆54Jul 21, 2025Updated 7 months ago
- A place to share problems solved with SciANN☆305Nov 6, 2023Updated 2 years ago
- ☆63Jul 24, 2019Updated 6 years ago
- Source code for deep learning-based reduced order models in cardiac electrophysiology. Available on doi.org/10.1371/journal.pone.0239416.☆16Sep 7, 2023Updated 2 years ago
- [NeurIPS 2024] Codebase for PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs.☆401Feb 11, 2026Updated 3 weeks ago
- hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations☆88Aug 26, 2025Updated 6 months ago
- Geometry-Aware Fourier Neural Operator (Geo-FNO)☆305Jun 2, 2025Updated 9 months ago
- Benchmarking of diffusion models for global field reconstruction from sparse observations☆31Dec 4, 2024Updated last year
- Model hub for all your DiffeqML needs. Pretrained weights, modules, and basic inference infrastructure☆28Mar 9, 2023Updated 3 years ago
- Time- and space-continuous neural PDE forecaster based on INRs and ODEs - ICLR 2023☆73Jan 13, 2024Updated 2 years ago