jorgeurban / self_scaled_algorithms_pinnsLinks
☆21Updated 5 months ago
Alternatives and similar repositories for self_scaled_algorithms_pinns
Users that are interested in self_scaled_algorithms_pinns are comparing it to the libraries listed below
Sorting:
- Transformed Generative Pre-Trained Physics-Informed Neural Networks (TGPT-PINN), a framework that extends Physics-Informed Neural Network…☆15Updated last year
- Sparse Physics-based and Interpretable Neural Networks☆50Updated 3 years ago
- ☆54Updated 2 years ago
- This repository contains the code and models for our paper "Investigating and Mitigating Failure Modes in Physics-informed Neural Network…☆19Updated last year
- ☆98Updated 3 years ago
- hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations☆81Updated 3 years ago
- Separabale Physics-Informed DeepONets in JAX☆10Updated 8 months ago
- Discontinuity Computing Using Physics-Informed Neural Network☆24Updated last year
- MIONet: Learning multiple-input operators via tensor product☆37Updated 2 years ago
- Multi-fidelity reduced-order surrogate modeling☆24Updated 2 months ago
- POD-PINN code and manuscript☆52Updated 9 months ago
- Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of n…☆35Updated 2 years ago
- hPINN: Physics-informed neural networks with hard constraints☆141Updated 3 years ago
- ☆11Updated last month
- ODIL (Optimizing a Discrete Loss) is a Python framework for solving inverse and data assimilation problems for partial differential equat…☆113Updated 3 weeks ago
- This is the repository for the code used in the ICML23 paper called "Achieving High Accuracy with PINNs via Energy Natural Gradient Desce…☆21Updated 10 months ago
- ☆50Updated 8 months ago
- Reliable extrapolation of deep neural operators informed by physics or sparse observations☆27Updated 2 years ago
- Codes associated with the manuscript titled "Multi-stage neural networks: Function approximator of machine precision"☆46Updated last year
- We propose a conservative physics-informed neural network (cPINN) on decompose domains for nonlinear conservation laws. The conservation …☆72Updated 2 years ago
- Python codes for Locally Adaptive Activation Function (LAAF) used in deep neural networks. Please cite this work as "A D Jagtap, K Kawa…☆41Updated 2 years ago
- PECANNs: Physics and Equality Constrained Artificial Neural Networks☆23Updated 2 years ago
- PDE Preserved Neural Network☆54Updated 3 months ago
- Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning☆89Updated 2 years ago
- In this repository, you will find the different python scripts to train the available models on the AirfRANS dataset proposed at the Neur…☆56Updated 7 months ago
- Gradient-based adaptive sampling algorithms for self-supervising PINNs☆26Updated 2 years ago
- ☆12Updated last year
- GCA-ROM is a library which implements graph convolutional autoencoder architecture as a nonlinear model order reduction strategy.☆36Updated 2 months ago
- ☆42Updated 2 years ago
- when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/1…☆75Updated 3 years ago