AmeyaJagtap / Locally-Adaptive-Activation-Functions-Neural-Networks-
Python codes for Locally Adaptive Activation Function (LAAF) used in deep neural networks. Please cite this work as "A D Jagtap, K Kawaguchi, G E Karniadakis, Locally adaptive activation functions with slope recovery for deep and physics-informed neural networks, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Scienc…
☆40Updated 2 years ago
Alternatives and similar repositories for Locally-Adaptive-Activation-Functions-Neural-Networks-
Users that are interested in Locally-Adaptive-Activation-Functions-Neural-Networks- are comparing it to the libraries listed below
Sorting:
- POD-PINN code and manuscript☆51Updated 6 months ago
- ☆53Updated 2 years ago
- Sparse Physics-based and Interpretable Neural Networks☆48Updated 3 years ago
- Multifidelity DeepONet☆32Updated last year
- Gradient-based adaptive sampling algorithms for self-supervising PINNs☆25Updated 2 years ago
- DeepONet extrapolation☆27Updated last year
- Competitive Physics Informed Networks☆30Updated 7 months ago
- ☆28Updated 2 years ago
- XPINN code written in TensorFlow 2☆27Updated 2 years ago
- Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.☆70Updated last year
- A collection of Jupyter notebooks providing tutorials on reduced order modeling techniques like DeepONet, FNO, DL-ROM, and POD-DL-ROM. Ea…☆25Updated 3 months ago
- hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations☆79Updated 2 years ago
- PECANNs: Physics and Equality Constrained Artificial Neural Networks☆22Updated last year
- Reproduce the first two numerical experiments(Pytorch)☆26Updated 4 years ago
- GCA-ROM is a library which implements graph convolutional autoencoder architecture as a nonlinear model order reduction strategy.☆36Updated last month
- This repository contains the code and models for our paper "Investigating and Mitigating Failure Modes in Physics-informed Neural Network…☆18Updated last year
- ☆21Updated 4 years ago
- Discontinuity Computing Using Physics-Informed Neural Network☆24Updated last year
- DAS: A deep adaptive sampling method for solving high-dimensional partial differential equations☆38Updated 5 months ago
- ☆62Updated 5 years ago
- Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data☆48Updated 4 years ago
- We propose a conservative physics-informed neural network (cPINN) on decompose domains for nonlinear conservation laws. The conservation …☆70Updated 2 years ago
- TensorFlow 2.0 implementation of Yibo Yang, Paris Perdikaris’s adversarial Uncertainty Quantification in Physics Informed Neural Networks…☆20Updated 2 years ago
- Coupled-Automatic-Numerical differentiation scheme for physics-informed neural network (can-PINN)☆28Updated last year
- Original implementation of fast PINN optimization with RBA weights☆52Updated 3 weeks ago
- MIONet: Learning multiple-input operators via tensor product☆34Updated 2 years ago
- A Backward Compatible -- Physics Informed Neural Network for Allen Cahn and Cahn Hilliard Equations☆30Updated 3 years ago
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆85Updated 4 years ago
- Code for reproducing the paper: RANG: A Residual-based Adaptive Node Generation Method for Physics-Informed Neural Networks☆15Updated 3 years ago
- Simplified implementation of locally adaptive activation functions (LAAF) with slope recovery for deep and physics-informed neural networ…☆30Updated 4 years ago