Jianxun-Wang / Physics-constrained-Bayesian-deep-learningLinks
Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data
☆50Updated 5 years ago
Alternatives and similar repositories for Physics-constrained-Bayesian-deep-learning
Users that are interested in Physics-constrained-Bayesian-deep-learning are comparing it to the libraries listed below
Sorting:
- Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning☆94Updated 2 years ago
- ☆63Updated 6 years ago
- POD-PINN code and manuscript☆57Updated last year
- This repository contains the files used in the paper " Reduced-order Model for Fluid Flows via Neural Ordinary Differential Equations"☆19Updated 2 years ago
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆92Updated 4 years ago
- Sparse Physics-based and Interpretable Neural Networks☆52Updated 4 years ago
- Multi-fidelity reduced-order surrogate modeling☆29Updated 7 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
- Sample codes of CNN-SINDy based reduced-order modeling for fluid flows by Fukami et al., JFM 2021.☆28Updated 4 years ago
- Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs☆93Updated 3 years ago
- TensorFlow 2.0 implementation of Yibo Yang, Paris Perdikaris’s adversarial Uncertainty Quantification in Physics Informed Neural Networks…☆21Updated 2 years ago
- Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.☆75Updated 2 years ago
- ☆25Updated 5 years ago
- ☆54Updated 3 years ago
- ☆40Updated 2 years ago
- Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of n…☆39Updated 2 years ago
- ☆26Updated 7 years ago
- Source code for deep learning-based reduced order models for nonlinear time-dependent parametrized PDEs. Available on doi.org/10.1007/s10…☆26Updated 2 years ago
- Competitive Physics Informed Networks☆32Updated last year
- XPINN code written in TensorFlow 2☆28Updated 2 years ago
- Source code for POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decom…☆37Updated 2 years ago
- Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data☆150Updated 6 years ago
- when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/1…☆84Updated 3 years ago
- PINNs for 2D Incompressible Navier-Stokes Equation☆58Updated last year
- Machine learning of linear differential equations using Gaussian processes☆25Updated 7 years ago
- Multi-fidelity Generative Deep Learning Turbulent Flows☆37Updated 5 years ago
- Reliable extrapolation of deep neural operators informed by physics or sparse observations☆28Updated 2 years ago
- ☆118Updated 6 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 last year
- Source code for the paper "Data-driven reduced-order models via regularised Operator Inference for a single-injector combustion process" …☆33Updated 3 years ago