Transformers for modeling physical systems
☆149Aug 4, 2023Updated 2 years ago
Alternatives and similar repositories for transformer-physx
Users that are interested in transformer-physx are comparing it to the libraries listed below. We may earn a commission when you buy through links labeled 'Ad' on this page.
Sorting:
- ☆11Jun 4, 2024Updated 2 years ago
- Multi-fidelity Generative Deep Learning Turbulent Flows☆37Jan 13, 2021Updated 5 years ago
- neural networks to learn Koopman eigenfunctions☆486Mar 22, 2024Updated 2 years ago
- Deep learning assisted dynamic mode decomposition☆19Oct 5, 2021Updated 4 years ago
- IIB Master's Project: Deep Learning for Koopman Optimal Predictive Control☆52Nov 15, 2020Updated 5 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.
- PyTorch implementation of GMLS-Nets. Machine learning methods for scattered unstructured data sets. Methods for learning differential op…☆28Nov 14, 2025Updated 8 months ago
- Contact-Aware Symplectic Integrator Network☆18Mar 22, 2023Updated 3 years ago
- This is a Python realization for Milan Korda and Igor Mezic's paper Linear predictors for nonlinear dynamical systems: Koopman operator m…☆14Apr 5, 2023Updated 3 years ago
- [NeurIPS 2021] Galerkin Transformer: Neural Operator built on Attention for PDEs☆266Jun 14, 2024Updated 2 years ago
- Solving inverse problems using conditional invertible neural networks.☆33Mar 23, 2021Updated 5 years ago
- Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification☆110Aug 4, 2020Updated 5 years ago
- Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning☆18Jan 9, 2024Updated 2 years ago
- Code and files related to random side projects☆21Jan 12, 2022Updated 4 years ago
- ☆63Jul 24, 2019Updated 6 years ago
- GPUs on demand by Runpod - Special Offer Available • AdRun AI, ML, and HPC workloads on powerful cloud GPUs—without limits or wasted spend. Deploy GPUs in under a minute and pay by the second.
- Consistent Koopman Autoencoders☆76May 23, 2023Updated 3 years ago
- Extraction of mechanical properties of materials through deep learning from instrumented indentation☆71Mar 16, 2022Updated 4 years ago
- Repo to the paper "Lie Point Symmetry Data Augmentation for Neural PDE Solvers"☆58May 23, 2023Updated 3 years ago
- Benchmark for learning stiff problems using physics-informed machine learning☆13Dec 15, 2021Updated 4 years ago
- Nonlinear proper orthogonal decomposition for convection-dominated flows☆15Nov 8, 2021Updated 4 years ago
- A library for Koopman Neural Operator with Pytorch.☆331Oct 5, 2024Updated last year
- Pseudospectral Kolmogorov Flow Solver☆43Oct 24, 2023Updated 2 years ago
- Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs☆98May 11, 2022Updated 4 years ago
- Tensor Basis Neural Network for Scalar Mixing☆10Mar 24, 2023Updated 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.
- Source code for deep learning-based reduced order models in cardiac electrophysiology. Available on doi.org/10.1371/journal.pone.0239416.☆17Sep 7, 2023Updated 2 years ago
- ☆20May 25, 2024Updated 2 years ago
- Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning☆98Aug 17, 2023Updated 2 years ago
- Code for Rice et al. 2020 "Analyzing Koopman approaches to physics-informed machine learning for long-term sea-surface temperature forcea…☆39Sep 9, 2025Updated 10 months ago
- NeurIPS-2025☆21Nov 4, 2025Updated 8 months ago
- Numerical assessments of a nonintrusive surrogate model based on recurrent neural networks and proper orthogonal decomposition: Rayleigh …☆10Dec 2, 2022Updated 3 years ago
- PaddleScience is SDK and library for developing AI-driven scientific computing applications based on PaddlePaddle.☆448Updated this week
- [ICLR 2020] Learning Compositional Koopman Operators for Model-Based Control☆94Apr 5, 2021Updated 5 years ago
- ☆21Dec 8, 2022Updated 3 years ago
- Wordpress hosting with auto-scaling - Free Trial Offer • AdFully Managed hosting for WordPress and WooCommerce businesses that need reliable, auto-scalable performance. Cloudways SafeUpdates now available.
- Deep learning for Engineers - Physics Informed Deep Learning☆367Dec 20, 2023Updated 2 years ago
- All the code available to reproduce the paper "Deep Learning Surrogate of Computational Fluid Dynamics for Thrombus Formation Risk in the…☆13Nov 7, 2019Updated 6 years ago
- Learning in infinite dimension with neural operators.☆3,761Jul 7, 2026Updated 2 weeks ago
- Source code for deep learning-based reduced order models for nonlinear time-dependent parametrized PDEs. Available on doi.org/10.1007/s10…☆28Sep 7, 2023Updated 2 years ago
- Time- and space-continuous neural PDE forecaster based on INRs and ODEs - ICLR 2023☆74Jan 13, 2024Updated 2 years ago
- Model-based Control using Koopman Operators☆57Jun 13, 2020Updated 6 years ago
- Sparsity-promoting Kernel Dynamic Mode Decomposition for Nonlinear Dynamical Systems☆29Jul 30, 2022Updated 3 years ago