Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
☆151Oct 18, 2019Updated 6 years ago
Alternatives and similar repositories for pde-surrogate
Users that are interested in pde-surrogate are comparing it to the libraries listed below. We may earn a commission when you buy through links labeled 'Ad' on this page.
Sorting:
- Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification☆108Aug 4, 2020Updated 5 years ago
- Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs☆93May 11, 2022Updated 3 years ago
- Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning☆95Aug 17, 2023Updated 2 years ago
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆92Jan 24, 2021Updated 5 years ago
- Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data☆50Jul 23, 2020Updated 5 years ago
- 1-Click AI Models by DigitalOcean Gradient • AdDeploy popular AI models on DigitalOcean Gradient GPU virtual machines with just a single click and start building anything your business needs.
- ☆63Jul 24, 2019Updated 6 years ago
- ☆26Oct 15, 2020Updated 5 years ago
- ☆111Oct 16, 2021Updated 4 years ago
- Deep residual networks for dimensionality reduction and surrogate modeling in high-dimensional inverse problems☆10Mar 20, 2021Updated 5 years ago
- Multi-fidelity Generative Deep Learning Turbulent Flows☆37Jan 13, 2021Updated 5 years ago
- POD-PINN code and manuscript☆58Nov 10, 2024Updated last year
- ☆13Nov 20, 2019Updated 6 years ago
- ☆247Oct 14, 2021Updated 4 years ago
- Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs☆169May 15, 2024Updated last year
- 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.
- ☆46Dec 7, 2022Updated 3 years ago
- Physics-informed neural networks with hard constraints for inverse design☆155Nov 21, 2021Updated 4 years ago
- Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonli…☆251Feb 1, 2023Updated 3 years ago
- This is the implementation of the PI-UNet for HSL-TFP☆27Feb 19, 2023Updated 3 years ago
- 3D CNN to predict single-phase flow velocity fields☆77Dec 7, 2022Updated 3 years ago
- ☆26Jun 16, 2018Updated 7 years ago
- A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data☆364Jul 14, 2023Updated 2 years ago
- Pytorch implementation of the DeepMoD algorithm: [arXiv:1904.09406]☆34Oct 30, 2023Updated 2 years ago
- physics-informed neural network for elastodynamics problem☆160Jan 20, 2022Updated 4 years ago
- Bare Metal GPUs on DigitalOcean Gradient AI • AdPurpose-built for serious AI teams training foundational models, running large-scale inference, and pushing the boundaries of what's possible.
- Deep Learning of Vortex Induced Vibrations☆99Feb 21, 2020Updated 6 years ago
- Neural operator learning of heterogeneous mechanobiological insults contributing to aortic aneurysms☆12Nov 4, 2024Updated last year
- ☆519Apr 1, 2025Updated 11 months ago
- Python codes for Locally Adaptive Activation Function (LAAF) used in deep neural networks. Please cite this work as "A D Jagtap, K Kawa…☆44Feb 1, 2023Updated 3 years ago
- PDEBench: An Extensive Benchmark for Scientific Machine Learning☆1,087Feb 24, 2026Updated last month
- ☆56Oct 9, 2022Updated 3 years ago
- ☆169Jun 27, 2022Updated 3 years ago
- ☆23Sep 29, 2021Updated 4 years ago
- An automatic knowledge embedding framework for scientific machine learning☆23May 15, 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.
- ☆20Feb 16, 2026Updated last month
- Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.☆78Aug 4, 2023Updated 2 years ago
- A library for scientific machine learning and physics-informed learning☆3,984Mar 1, 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 7 months ago
- Learning in infinite dimension with neural operators.☆3,486Feb 24, 2026Updated last month
- ☆398Dec 3, 2022Updated 3 years ago
- ☆117Feb 5, 2025Updated last year