juansensio / nangsLinks
Solving PDEs with NNs
☆55Updated 2 years ago
Alternatives and similar repositories for nangs
Users that are interested in nangs are comparing it to the libraries listed below
Sorting:
- Hidden physics models: Machine learning of nonlinear partial differential equations☆147Updated 5 years ago
- Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs☆93Updated 3 years ago
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆88Updated 4 years ago
- hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations☆84Updated 2 months ago
- Sparse Physics-based and Interpretable Neural Networks☆52Updated 4 years ago
- Deep learning library for solving differential equations on top of PyTorch.☆61Updated 5 years ago
- ☆99Updated 4 years ago
- Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning☆92Updated 2 years ago
- ☆226Updated 4 years ago
- ☆116Updated 6 years ago
- ☆63Updated 6 years ago
- Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data☆150Updated 6 years ago
- ☆71Updated last year
- Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonli…☆217Updated 2 years ago
- ☆152Updated 3 years ago
- hPINN: Physics-informed neural networks with hard constraints☆147Updated 3 years ago
- when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/1…☆78Updated 3 years ago
- Example problems in Physics informed neural network in JAX☆81Updated 2 years ago
- ☆107Updated last year
- Applications of PINOs☆138Updated 3 years ago
- Reliable extrapolation of deep neural operators informed by physics or sparse observations☆27Updated 2 years ago
- We propose a conservative physics-informed neural network (cPINN) on decompose domains for nonlinear conservation laws. The conservation …☆74Updated 2 years ago
- Neural network based solvers for partial differential equations and inverse problems . Implementation of physics-informed neural networks…☆157Updated 9 months ago
- Deep Learning for Reduced Order Modelling☆100Updated 3 years ago
- ☆114Updated 8 months ago
- ODIL (Optimizing a Discrete Loss) is a Python framework for solving inverse and data assimilation problems for partial differential equat…☆117Updated 2 weeks ago
- Easy Reduced Basis method☆88Updated 2 months ago
- Deep learning for Engineers - Physics Informed Deep Learning☆351Updated last year
- Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data☆49Updated 5 years ago
- ☆50Updated 2 years ago