zabaras / deep-turbulenceLinks
Multi-fidelity Generative Deep Learning Turbulent Flows
☆38Updated 4 years ago
Alternatives and similar repositories for deep-turbulence
Users that are interested in deep-turbulence are comparing it to the libraries listed below
Sorting:
- Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs☆93Updated 3 years ago
- Sparse Physics-based and Interpretable Neural Networks☆51Updated 3 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
- Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data☆49Updated 5 years ago
- Source code for the paper "Data-driven reduced-order models via regularised Operator Inference for a single-injector combustion process" …☆32Updated 3 years ago
- POD-PINN code and manuscript☆52Updated 9 months ago
- In this repository, you will find the different python scripts to train the available models on the AirfRANS dataset proposed at the Neur…☆56Updated 7 months ago
- XPINN code written in TensorFlow 2☆28Updated 2 years ago
- ☆63Updated 6 years ago
- Tensor Basis Neural Network for Scalar Mixing☆10Updated 2 years ago
- TensorFlow 2.0 implementation of Yibo Yang, Paris Perdikaris’s adversarial Uncertainty Quantification in Physics Informed Neural Networks…☆20Updated 2 years ago
- Turbulent flow network source code☆70Updated 5 months ago
- Source code for "Probabilistic neural networks for fluid flow model-order reduction and data recovery"☆11Updated 4 years ago
- Multi-fidelity reduced-order surrogate modeling☆24Updated 2 months ago
- Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning☆89Updated 2 years ago
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆87Updated 4 years ago
- Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of n…☆35Updated 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
- ☆21Updated 4 years ago
- Reliable extrapolation of deep neural operators informed by physics or sparse observations☆27Updated 2 years ago
- ☆27Updated last year
- Discontinuity Computing Using Physics-Informed Neural Network☆24Updated last year
- Physics-Informed Neural Networks for solving PDEs (bachelor project)☆10Updated 2 years ago
- MIONet: Learning multiple-input operators via tensor product☆37Updated 2 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
- Reduced-Order Modeling of Fluid Flows with Transformers☆24Updated 2 years ago
- Source code for POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decom…☆32Updated last year
- ☆25Updated 7 years ago
- ☆54Updated 2 years ago
- Data-driven Reynolds stress modeling with physics-informed machine learning☆93Updated 6 years ago