AmeyaJagtap / Rowdy_Activation_FunctionsLinks
We propose Deep Kronecker Neural Network, which is a general framework for neural networks with adaptive activation functions. In particular we proposed Rowdy activation functions that inject sinusoidal fluctuations thereby allows the optimizer to exploit more and train the network faster. Various test cases ranging from function approximation, …
☆12Updated 2 years ago
Alternatives and similar repositories for Rowdy_Activation_Functions
Users that are interested in Rowdy_Activation_Functions are comparing it to the libraries listed below
Sorting:
- Reliable extrapolation of deep neural operators informed by physics or sparse observations☆27Updated 2 years ago
- POD-PINN code and manuscript☆52Updated 9 months ago
- Physics-informed deep super-resolution of spatiotemporal data☆46Updated last year
- Physics-encoded recurrent convolutional neural network☆46Updated 3 years ago
- PDE Preserved Neural Network☆54Updated 3 months ago
- Laminar flow prediction using graph neural networks☆31Updated 6 months ago
- Official implementation of "PhyGNNet: Solving spatiotemporal PDEs with Physics-informed Graph Neural Network"☆48Updated 2 years ago
- XPINN code written in TensorFlow 2☆28Updated 2 years ago
- ☆64Updated 2 years ago
- Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data☆49Updated 5 years ago
- Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning☆89Updated last year
- This repo contains a PyTorch-based AE-ConvLSTM model for fluid flow prediction. It can forecast 5–10 time steps per forward pass and over…☆24Updated 2 months ago
- PINNs for 2D Incompressible Navier-Stokes Equation☆51Updated last year
- Multi-fidelity Generative Deep Learning Turbulent Flows☆38Updated 4 years ago
- MIONet: Learning multiple-input operators via tensor product☆37Updated 2 years ago
- KTH-FlowAI / beta-Variational-autoencoders-and-transformers-for-reduced-order-modelling-of-fluid-flows☆33Updated 3 months ago
- Sparse Physics-based and Interpretable Neural Networks☆50Updated 3 years ago
- ☆56Updated 6 months ago
- Reduced-Order Modeling of Fluid Flows with Transformers☆24Updated 2 years ago
- This is the implementation of the RecFNO.☆21Updated 2 years ago
- Original implementation of fast PINN optimization with RBA weights☆57Updated 3 months ago
- Learning two-phase microstructure evolution using neural operators and autoencoder architectures☆23Updated last year
- ☆27Updated last year
- Gradient-based adaptive sampling algorithms for self-supervising PINNs☆27Updated 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
- Generative Adversarial Networks are used to super resolve turbulent flow fields from low resolution (RANS/LES) fields to high resolution …☆23Updated 4 years ago
- This repository contains the files used in the paper " Reduced-order Model for Fluid Flows via Neural Ordinary Differential Equations"☆19Updated 2 years ago
- Research project conducted at Pacific Northwest National Laboratory, exploring the use of physics-informed autoencoders to predict fluid …☆36Updated 2 years ago
- Physics Informed Fourier Neural Operator☆24Updated 8 months ago
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆87Updated 4 years ago