AmeyaJagtap / Locally-Adaptive-Activation-Functions-Neural-Networks-Links
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…
☆41Updated 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☆52Updated 9 months ago
- Sparse Physics-based and Interpretable Neural Networks☆50Updated 3 years ago
- Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning☆89Updated last year
- Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of n…☆35Updated 2 years ago
- ☆54Updated 2 years ago
- ☆63Updated 6 years ago
- Gradient-based adaptive sampling algorithms for self-supervising PINNs☆27Updated 2 years ago
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆87Updated 4 years ago
- Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data☆49Updated 5 years ago
- This repository contains the code and models for our paper "Investigating and Mitigating Failure Modes in Physics-informed Neural Network…☆18Updated last year
- DAS: A deep adaptive sampling method for solving high-dimensional partial differential equations☆38Updated 8 months ago
- hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations☆81Updated 3 years ago
- ☆21Updated 4 years ago
- Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.☆73Updated 2 years ago
- XPINN code written in TensorFlow 2☆28Updated 2 years ago
- Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems☆96Updated 3 years ago
- Reliable extrapolation of deep neural operators informed by physics or sparse observations☆27Updated 2 years ago
- A Backward Compatible -- Physics Informed Neural Network for Allen Cahn and Cahn Hilliard Equations☆32Updated 3 years ago
- Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems☆56Updated 3 years ago
- Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs☆93Updated 3 years ago
- Source code for deep learning-based reduced order models for nonlinear time-dependent parametrized PDEs. Available on doi.org/10.1007/s10…☆25Updated last year
- Pytorch implementation of Bayesian physics-informed neural networks☆61Updated 3 years ago
- A collection of Jupyter notebooks providing tutorials on reduced order modeling techniques like DeepONet, FNO, DL-ROM, and POD-DL-ROM. Ea…☆25Updated 6 months ago
- ☆37Updated last year
- Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data☆150Updated 5 years ago
- ☆97Updated 3 years ago
- ☆146Updated 3 years ago
- ☆29Updated 2 years ago
- Examplary code for NN, MFNN, DynNet, PINNs and CNN☆49Updated 3 years ago
- PECANNs: Physics and Equality Constrained Artificial Neural Networks☆23Updated 2 years ago