pkmtum / Semi-supervised_Invertible_Neural_Operators
Semi-supervised Invertible Neural Operators for Bayesian Inverse Problems
☆13Updated 8 months ago
Alternatives and similar repositories for Semi-supervised_Invertible_Neural_Operators:
Users that are interested in Semi-supervised_Invertible_Neural_Operators are comparing it to the libraries listed below
- ☆37Updated 2 years ago
- ☆30Updated last year
- Learning two-phase microstructure evolution using neural operators and autoencoder architectures☆23Updated 9 months ago
- Code for "Beyond Regular Grids: Fourier-Based Neural Operators on Arbitrary Domains"☆17Updated 9 months ago
- ☆12Updated last year
- ☆52Updated 2 years ago
- ☆22Updated 7 months ago
- Domain Agnostic Fourier Neural Operators (DAFNO)☆13Updated 5 months ago
- Learning with Higher Expressive Power than Neural Networks (On Learning PDEs)☆15Updated 3 years ago
- This repository contains the code and models for our paper "Investigating and Mitigating Failure Modes in Physics-informed Neural Network…☆17Updated last year
- ☆18Updated last year
- Sparse Physics-based and Interpretable Neural Networks☆47Updated 3 years ago
- DeepONet extrapolation☆25Updated last year
- Variational Neural Networks for the Solution of Partial Differential Equations☆8Updated 5 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…☆38Updated 2 years ago
- GCA-ROM is a library which implements graph convolutional autoencoder architecture as a nonlinear model order reduction strategy.☆31Updated 7 months ago
- ☆11Updated last month
- This is the implementation of the RecFNO.☆17Updated last year
- PDE-VAE: Variational Autoencoder for Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning☆33Updated 3 years ago
- Practicum on Supervised Learning in Function Spaces☆32Updated 2 years ago
- Source code of "Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems."☆55Updated 6 months ago
- ☆25Updated 2 years ago
- A sequential DeepONet model implementation that uses a recurrent neural network (GRU and LSTM) in the branch and a feed-forward neural ne…☆12Updated 9 months ago
- Coupled-Automatic-Numerical differentiation scheme for physics-informed neural network (can-PINN)☆25Updated last year
- ☆21Updated 3 years ago
- An extension of Fourier Neural Operator to finite-dimensional input and/or output spaces.☆17Updated 8 months ago
- Dimension reduced surrogate construction for parametric PDE maps☆38Updated 3 weeks ago
- Accelerating Physics Informed Neural Networks (PINNs) using Meshless Discretizations☆22Updated last year
- ☆46Updated 3 weeks ago