Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations
☆293Jul 30, 2022Updated 3 years ago
Alternatives and similar repositories for DeepHPMs
Users that are interested in DeepHPMs are comparing it to the libraries listed below. We may earn a commission when you buy through links labeled 'Ad' on this page.
Sorting:
- Hidden physics models: Machine learning of nonlinear partial differential equations☆152Feb 20, 2020Updated 6 years ago
- Tutorial on a number of topics in Deep Learning☆38Feb 20, 2020Updated 6 years ago
- Forward-Backward Stochastic Neural Networks: Deep Learning of High-dimensional Partial Differential Equations☆160Feb 20, 2020Updated 6 years ago
- Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems☆67Feb 20, 2020Updated 6 years ago
- Deep Learning of Turbulent Scalar Mixing☆19Oct 6, 2019Updated 6 years ago
- Deploy on Railway without the complexity - Free Credits Offer • AdConnect your repo and Railway handles the rest with instant previews. Quickly provision container image services, databases, and storage volumes.
- Deep Learning of Vortex Induced Vibrations☆102Feb 21, 2020Updated 6 years ago
- Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations☆6,054Feb 11, 2026Updated 5 months ago
- Machine learning of linear differential equations using Gaussian processes☆27May 2, 2018Updated 8 years ago
- Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations☆71May 26, 2020Updated 6 years ago
- Hidden Fluid Mechanics☆372Jan 30, 2023Updated 3 years ago
- Parametric Gaussian Process Regression for Big Data☆47Feb 20, 2020Updated 6 years ago
- A library for scientific machine learning and physics-informed learning☆4,325Updated this week
- TensorFlow 2.0 implementation of Maziar Raissi's Physics Informed Neural Networks (PINNs).☆267Nov 30, 2023Updated 2 years ago
- PyDEns is a framework for solving Ordinary and Partial Differential Equations (ODEs & PDEs) using neural networks☆316Feb 9, 2024Updated 2 years ago
- Virtual machines for every use case on DigitalOcean • AdGet dependable uptime with 99.99% SLA, simple security tools, and predictable monthly pricing with DigitalOcean's virtual machines, called Droplets.
- Code for "Learning data-driven discretizations for partial differential equations"☆167Aug 19, 2025Updated 11 months ago
- Parametric Gaussian Process Regression for Big Data (Matlab Version)☆25May 2, 2018Updated 8 years ago
- ☆280Nov 4, 2022Updated 3 years ago
- ☆116Oct 16, 2021Updated 4 years ago
- Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonli…☆268Feb 1, 2023Updated 3 years ago
- ☆19Oct 17, 2021Updated 4 years ago
- Tutorial on Gaussian Processes☆67Feb 20, 2020Updated 6 years ago
- Solving High Dimensional Partial Differential Equations with Deep Neural Networks☆34Dec 21, 2021Updated 4 years ago
- Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems☆112Apr 15, 2022Updated 4 years ago
- Open source password manager - Proton Pass • AdSecurely store, share, and autofill your credentials with Proton Pass, the end-to-end encrypted password manager trusted by millions.
- Implementation of the paper "Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism" [AAAI-MLPS 2021]☆298Oct 12, 2021Updated 4 years ago
- ☆269Oct 14, 2021Updated 4 years ago
- PDE-Net: Learning PDEs from Data☆332Jun 26, 2021Updated 5 years ago
- Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated s…☆1,211Updated this week
- Multi-fidelity Generative Deep Learning Turbulent Flows☆37Jan 13, 2021Updated 5 years ago
- ☆52Nov 6, 2023Updated 2 years ago
- XPINN code written in TensorFlow 2☆28Feb 1, 2023Updated 3 years ago
- Companion code for "Solving Nonlinear and High-Dimensional Partial Differential Equations via Deep Learning" by A. Al-Aradi, A. Correia, …☆126Jul 30, 2019Updated 6 years ago
- Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs☆98May 11, 2022Updated 4 years ago
- Deploy on Railway without the complexity - Free Credits Offer • AdConnect your repo and Railway handles the rest with instant previews. Quickly provision container image services, databases, and storage volumes.
- Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed c…☆118Mar 1, 2022Updated 4 years ago
- Deep learning for Engineers - Physics Informed Deep Learning☆367Dec 20, 2023Updated 2 years ago
- ☆32Oct 6, 2022Updated 3 years ago
- Group project for Deep Learning: Algorithms and Applications in Peking University 2018 Spring. This is a brief survey, discussion and imp…☆48Jul 2, 2018Updated 8 years ago
- Code for the paper "The Random Feature Model for Input-Output Maps between Banach Spaces" (SIREV SIGEST 2024, SISC 2021)☆15Aug 8, 2024Updated last year
- ☆119Jul 28, 2019Updated 7 years ago
- We propose a conservative physics-informed neural network (cPINN) on decompose domains for nonlinear conservation laws. The conservation …☆82Feb 1, 2023Updated 3 years ago