JRice15 / physics-informed-autoencodersLinks
Research project conducted at Pacific Northwest National Laboratory, exploring the use of physics-informed autoencoders to predict fluid flow dynamics
☆35Updated 2 years ago
Alternatives and similar repositories for physics-informed-autoencoders
Users that are interested in physics-informed-autoencoders are comparing it to the libraries listed below
Sorting:
- Official implementation of "PhyGNNet: Solving spatiotemporal PDEs with Physics-informed Graph Neural Network"☆46Updated 2 years ago
- Physics-encoded recurrent convolutional neural network☆46Updated 3 years ago
- 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…☆23Updated this week
- Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data☆49Updated 4 years ago
- Deep learning assisted dynamic mode decomposition☆19Updated 3 years ago
- A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery.☆26Updated 3 years ago
- Physics Informed Fourier Neural Operator☆21Updated 6 months ago
- We introduce an innovative physics-informed LSTM framework for metamodeling of nonlinear structural systems with scarce data.☆85Updated last year
- Physics-informed deep super-resolution of spatiotemporal data☆45Updated last year
- ☆24Updated 2 years ago
- ☆29Updated last year
- Pytorch implementation of Bayesian physics-informed neural networks☆59Updated 3 years ago
- ☆55Updated 4 months ago
- The code for the paper Temperature field inversion of heat-source systems via physics-informed neural networks☆36Updated 2 years ago
- Supporting code for "Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders"☆21Updated 4 years ago
- Implementing a physics-informed DeepONet from scratch☆40Updated last year
- Boosting the training of physics informed neural networks with transfer learning☆26Updated 4 years ago
- ☆19Updated 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 4 months ago
- Sparse Physics-based and Interpretable Neural Networks☆50Updated 3 years ago
- ☆62Updated 2 years ago
- Towards Physics-informed Deep Learning for Turbulent Flow Prediction☆26Updated 3 years ago
- POD-PINN code and manuscript☆51Updated 6 months ago
- Gradient-based adaptive sampling algorithms for self-supervising PINNs☆26Updated 2 years ago
- Research/development of physics-informed neural networks for dynamic systems☆23Updated 6 months ago
- KTH-FlowAI / beta-Variational-autoencoders-and-transformers-for-reduced-order-modelling-of-fluid-flows☆31Updated last month
- Source code of "Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems."☆62Updated last month
- Differentiable Physics-informed Graph Networks☆66Updated 5 years ago
- ☆63Updated 5 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