Rui1521 / Turbulent-Flow-NetsLinks
Towards Physics-informed Deep Learning for Turbulent Flow Prediction
☆27Updated 4 years ago
Alternatives and similar repositories for Turbulent-Flow-Nets
Users that are interested in Turbulent-Flow-Nets are comparing it to the libraries listed below
Sorting:
- Official implementation of "PhyGNNet: Solving spatiotemporal PDEs with Physics-informed Graph Neural Network"☆53Updated 2 years ago
- POD-PINN code and manuscript☆56Updated last year
- ☆54Updated 3 years ago
- Turbulent flow network source code☆71Updated 8 months ago
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆88Updated 4 years ago
- MIONet: Learning multiple-input operators via tensor product☆39Updated 3 years ago
- Reliable extrapolation of deep neural operators informed by physics or sparse observations☆28Updated 2 years ago
- ☆63Updated 6 years ago
- Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs☆93Updated 3 years ago
- In this repository, you will find the different python scripts to train the available models on the AirfRANS dataset proposed at the Neur…☆57Updated 11 months ago
- Laminar flow prediction using graph neural networks☆31Updated 10 months ago
- ☆13Updated 6 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
- GCA-ROM is a library which implements graph convolutional autoencoder architecture as a nonlinear model order reduction strategy.☆36Updated 3 weeks ago
- Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data☆49Updated 5 years ago
- Physics-encoded recurrent convolutional neural network☆47Updated 3 years ago
- ☆65Updated 3 months ago
- PDE Preserved Neural Network☆58Updated 6 months ago
- Python codes for Locally Adaptive Activation Function (LAAF) used in deep neural networks. Please cite this work as "A D Jagtap, K Kawa…☆43Updated 2 years ago
- Characterizing possible failure modes in physics-informed neural networks.☆145Updated 4 years ago
- Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data☆150Updated 6 years ago
- Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning☆93Updated 2 years ago
- Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.☆75Updated 2 years ago
- Source code of "Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems."☆75Updated 7 months ago
- Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems☆57Updated 3 years ago
- KTH-FlowAI / beta-Variational-autoencoders-and-transformers-for-reduced-order-modelling-of-fluid-flows☆40Updated 7 months ago
- Reduced-Order Modeling of Fluid Flows with Transformers☆24Updated 2 years ago
- Multi-fidelity reduced-order surrogate modeling☆28Updated 5 months ago
- Sparse Physics-based and Interpretable Neural Networks☆52Updated 4 years ago
- ☆67Updated 3 years ago