☆417Dec 3, 2022Updated 3 years ago
Alternatives and similar repositories for Physics-informed-DeepONets
Users that are interested in Physics-informed-DeepONets are comparing it to the libraries listed below. We may earn a commission when you buy through links labeled 'Ad' on this page.
Sorting:
- ☆55Oct 9, 2022Updated 3 years ago
- Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators☆802Jun 25, 2022Updated 3 years ago
- A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data☆376Jul 14, 2023Updated 2 years ago
- ☆536Apr 1, 2025Updated last year
- Implementing a physics-informed DeepONet from scratch☆61Jul 9, 2023Updated 2 years ago
- Serverless GPU API endpoints on Runpod - Get Bonus Credits • AdSkip the infrastructure headaches. Auto-scaling, pay-as-you-go, no-ops approach lets you focus on innovating your application.
- Physics Informed Machine Learning Tutorials (Pytorch and Jax)☆669Apr 27, 2026Updated 3 weeks ago
- ☆15Oct 25, 2021Updated 4 years ago
- ☆171Jun 27, 2022Updated 3 years ago
- A library for scientific machine learning and physics-informed learning☆4,151Mar 1, 2026Updated 2 months ago
- Applications of PINOs☆149Oct 10, 2022Updated 3 years ago
- Learning in infinite dimension with neural operators.☆3,632May 11, 2026Updated last week
- Separabale Physics-Informed DeepONets in JAX☆24Nov 29, 2024Updated last year
- Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of n…☆40Jul 12, 2023Updated 2 years ago
- ☆215Feb 16, 2024Updated 2 years ago
- End-to-end encrypted cloud storage - Proton Drive • AdSpecial offer: 40% Off Yearly / 80% Off First Month. Protect your most important files, photos, and documents from prying eyes.
- ☆1,097Apr 9, 2026Updated last month
- Geometry-Aware Fourier Neural Operator (Geo-FNO)☆331Apr 16, 2026Updated last month
- A Deep Learning Approach to Solving PDEs: Implementing Neural Networks with Pytorch and Jax☆10Apr 27, 2023Updated 3 years ago
- Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.☆83Aug 4, 2023Updated 2 years ago
- Implementation of the paper "Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism" [AAAI-MLPS 2021]☆293Oct 12, 2021Updated 4 years ago
- ☆260Oct 14, 2021Updated 4 years ago
- ☆26Jul 7, 2022Updated 3 years ago
- Using graph network to solve PDEs☆446Jun 2, 2025Updated 11 months ago
- Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations☆5,851Feb 11, 2026Updated 3 months ago
- Managed Database hosting by DigitalOcean • AdPostgreSQL, MySQL, MongoDB, Kafka, Valkey, and OpenSearch available. Automatically scale up storage and focus on building your apps.
- Physics-informed neural networks with hard constraints for inverse design☆158Nov 21, 2021Updated 4 years ago
- [NeurIPS 2021] Galerkin Transformer: Neural Operator built on Attention for PDEs☆263Jun 14, 2024Updated last year
- Simple demo on implementing data driven and physics informed Deep O Nets in pytorch☆21Jun 20, 2024Updated last year
- PDEBench: An Extensive Benchmark for Scientific Machine Learning☆1,134Mar 30, 2026Updated last month
- DON-LSTM: Multi-Resolution Learning with DeepONets and Long-Short Term Memory Neural Networks☆10Sep 4, 2025Updated 8 months ago
- ☆426Nov 14, 2025Updated 6 months ago
- A sequential DeepONet model implementation that uses a recurrent neural network (GRU and LSTM) in the branch and a feed-forward neural ne…☆20May 3, 2024Updated 2 years ago
- Original implementation of fast PINN optimization with RBA weights☆73Sep 11, 2025Updated 8 months ago
- ☆33Oct 6, 2022Updated 3 years ago
- GPU virtual machines on DigitalOcean Gradient AI • AdGet to production fast with high-performance AMD and NVIDIA GPUs you can spin up in seconds. The definition of operational simplicity.
- ☆12May 3, 2023Updated 3 years ago
- Investigating PINNs☆760Aug 13, 2021Updated 4 years ago
- Using Physics-Informed Deep Learning (PIDL) techniques (W-PINNs-DE & W-PINNs) to solve forward and inverse hydrodynamic shock-tube proble…☆224Feb 21, 2023Updated 3 years ago
- ☆17Aug 6, 2024Updated last year
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆91Jan 24, 2021Updated 5 years ago
- Characterizing possible failure modes in physics-informed neural networks.☆152Nov 18, 2021Updated 4 years ago
- DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia☆293Sep 28, 2024Updated last year