JRice15 / physics-informed-autoencoders
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
- Physics-encoded recurrent convolutional neural network☆45Updated 3 years ago
- A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery.☆25Updated 3 years ago
- Official implementation of "PhyGNNet: Solving spatiotemporal PDEs with Physics-informed Graph Neural Network"☆42Updated last year
- Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data☆48Updated 4 years ago
- Physics-informed deep super-resolution of spatiotemporal data☆41Updated last year
- Deep learning assisted dynamic mode decomposition☆19Updated 3 years ago
- ☆28Updated last year
- Supporting code for "Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders"☆21Updated 4 years ago
- Physics Informed Fourier Neural Operator☆19Updated 4 months ago
- Differentiable Physics-informed Graph Networks☆65Updated 5 years ago
- This repository contains an Auto-encoder ConvLSTM network (Pytorch) which can be used to predict a large number of time steps (100+). The…☆21Updated 2 years ago
- The code for the paper Temperature field inversion of heat-source systems via physics-informed neural networks☆32Updated 2 years ago
- ☆13Updated 5 years ago
- Towards Physics-informed Deep Learning for Turbulent Flow Prediction☆24Updated 3 years ago
- Boosting the training of physics informed neural networks with transfer learning☆26Updated 3 years ago
- Pytorch implementation of Bayesian physics-informed neural networks☆56Updated 3 years ago
- when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/1…☆67Updated 2 years ago
- ☆53Updated 2 years ago
- ☆19Updated 3 years ago
- ☆47Updated 2 months ago
- Implementing a physics-informed DeepONet from scratch☆37Updated last year
- Consistent Koopman Autoencoders☆71Updated last year
- ☆62Updated 5 years ago
- Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs☆31Updated 3 years ago
- Source code of "Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems."☆58Updated 8 months ago
- Learning two-phase microstructure evolution using neural operators and autoencoder architectures☆23Updated 11 months ago
- POD-PINN code and manuscript☆49Updated 4 months ago
- A collection of Jupyter notebooks providing tutorials on reduced order modeling techniques like DeepONet, FNO, DL-ROM, and POD-DL-ROM. Ea…☆25Updated 2 months ago
- Sparse Physics-based and Interpretable Neural Networks☆48Updated 3 years ago
- This repository contains the files used in the paper " Reduced-order Model for Fluid Flows via Neural Ordinary Differential Equations"☆18Updated 2 years ago