maziarraissi / DeepHPMsLinks
Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations
☆280Updated 3 years ago
Alternatives and similar repositories for DeepHPMs
Users that are interested in DeepHPMs are comparing it to the libraries listed below
Sorting:
- Hidden physics models: Machine learning of nonlinear partial differential equations☆147Updated 5 years ago
- ☆265Updated 2 years ago
- TensorFlow 2.0 implementation of Maziar Raissi's Physics Informed Neural Networks (PINNs).☆262Updated last year
- Deep learning for Engineers - Physics Informed Deep Learning☆351Updated last year
- ☆226Updated 4 years ago
- Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data☆150Updated 6 years ago
- Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonli…☆217Updated 2 years ago
- PDE-Net: Learning PDEs from Data☆322Updated 4 years ago
- ☆116Updated 6 years ago
- Characterizing possible failure modes in physics-informed neural networks.☆143Updated 3 years ago
- ☆153Updated 3 years ago
- IDRLnet, a Python toolbox for modeling and solving problems through Physics-Informed Neural Network (PINN) systematically.☆241Updated last year
- ☆63Updated 6 years ago
- Hidden Fluid Mechanics☆336Updated 2 years ago
- Neural network based solvers for partial differential equations and inverse problems . Implementation of physics-informed neural networks…☆158Updated 9 months ago
- hPINN: Physics-informed neural networks with hard constraints☆147Updated 3 years ago
- ☆195Updated 7 months ago
- Implementation of the paper "Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism" [AAAI-MLPS 2021]☆254Updated 4 years ago
- PyDEns is a framework for solving Ordinary and Partial Differential Equations (ODEs & PDEs) using neural networks☆313Updated last year
- Code for "Learning data-driven discretizations for partial differential equations"☆168Updated 2 months ago
- ☆131Updated 3 years ago
- Forward-Backward Stochastic Neural Networks: Deep Learning of High-dimensional Partial Differential Equations☆156Updated 5 years ago
- Physics informed neural network (PINN) for cavity flow governed by Navier-Stokes equation.☆152Updated 5 years ago
- Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification☆107Updated 5 years ago
- ☆99Updated 4 years ago
- This repository contains a number of Jupyter Notebooks illustrating different approaches to solve partial differential equations by means…☆179Updated 4 years ago
- Physics-informed learning of governing equations from scarce data☆156Updated 2 years ago
- Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs☆93Updated 3 years ago
- ☆366Updated 2 years ago
- Tutorials on deep learning, Python, and dissipative particle dynamics☆199Updated 3 years ago