HwijaeSon / AL-PINNsLinks
Official code for AL-PINNS: Augmented Lagrangian relaxation method for Physics-Informed Neural Networks
☆11Updated 2 years ago
Alternatives and similar repositories for AL-PINNs
Users that are interested in AL-PINNs are comparing it to the libraries listed below
Sorting:
- Source code of: "Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models".☆38Updated 3 years ago
- Paper List of Physics-Informed Neural Network (PINN)☆40Updated 3 months ago
- 🌌 Applications of Physics-Informed ML: A collection of notebooks from my Masters research, exploring how machine learning can solve scie…☆11Updated 11 months ago
- Code for Learning Sparse Nonlinear Dynamics via Mixed Integer Optimization☆16Updated 3 years ago
- ☆15Updated 2 years ago
- AI4Science: Python/Matlab implementation of online and window dynamic mode decomposition (Online DMD and Window DMD)☆44Updated 3 years ago
- Sparsity-promoting Kernel Dynamic Mode Decomposition for Nonlinear Dynamical Systems☆30Updated 3 years ago
- This repository contains code for the paper "MAgNet: Mesh-Agnostic Neural PDE Solver" https://arxiv.org/abs/2210.05495☆37Updated 2 years ago
- ☆11Updated 2 years ago
- ☆23Updated 4 months ago
- Neural Galerkin☆16Updated 2 years ago
- ☆10Updated 2 years ago
- ☆15Updated last year
- ☆21Updated 2 years ago
- [ICLR 2024] Scaling physics-informed hard constraints with mixture-of-experts.☆35Updated last year
- A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery.☆27Updated 4 years ago
- Learning with Higher Expressive Power than Neural Networks (On Learning PDEs)☆16Updated 4 years ago
- ☆16Updated last year
- ☆21Updated 5 years ago
- ☆11Updated 4 years ago
- ☆30Updated 7 years ago
- Bayesian autoencoders for data-driven discovery of coordinates, governing equations and fundamental constants. Proceedings of the Royal S…☆10Updated last year
- SymDer: Symbolic Derivative Approach to Discovering Sparse Interpretable Dynamics from Partial Observations☆21Updated 3 years ago
- In this work, we present a novel approach that combines the power of Koopman operators and deep neural networks to generate a linear rep…☆10Updated last year
- ☆34Updated 3 years ago
- Official implementation of *A Unified Hard-Constraint Framework for Solving Geometrically Complex PDEs*☆16Updated 2 years ago
- Koopman Kernels for Learning Dynamical Systems from Trajectory Data☆31Updated last year
- ☆10Updated 2 years ago
- ☆29Updated last year
- Hands-on tutorial for implementing Physics Informed Neural Networks in Pytorch☆52Updated 6 months ago