Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning
☆100Aug 17, 2023Updated 3 years ago
Alternatives and similar repositories for LabelFree-DNN-Surrogate
Users that are interested in LabelFree-DNN-Surrogate are comparing it to the libraries listed below. We may earn a commission when you buy through links labeled 'Ad' on this page.
Sorting:
- Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data☆49Jul 23, 2020Updated 6 years ago
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆91Jan 24, 2021Updated 5 years ago
- Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data☆155Oct 18, 2019Updated 7 years ago
- ☆63Jul 24, 2019Updated 7 years ago
- POD-PINN code and manuscript☆62Nov 10, 2024Updated last year
- Deploy on Railway without the complexity - Free Credits Offer • AdConnect your repo and Railway handles the rest with instant previews. Quickly provision container image services, databases, and storage volumes.
- Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs☆97May 11, 2022Updated 4 years ago
- ☆119Oct 16, 2021Updated 5 years ago
- Physics-informed neural network for solving fluid dynamics problems☆294Jan 28, 2021Updated 5 years ago
- Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification☆111Aug 4, 2020Updated 6 years ago
- physics-informed neural network for elastodynamics problem☆164Jan 20, 2022Updated 4 years ago
- A place to share problems solved with SciANN☆311Nov 6, 2023Updated 3 years ago
- Numerical assessments of a nonintrusive surrogate model based on recurrent neural networks and proper orthogonal decomposition: Rayleigh …☆10Dec 2, 2022Updated 3 years ago
- ☆274Oct 14, 2021Updated 5 years ago
- We proposed the simple adaptive activation functions deep neural networks. The proposed method is simple and easy to implement in any neu…☆12Feb 1, 2023Updated 3 years ago
- Deploy on Railway without the complexity - Free Credits Offer • AdConnect your repo and Railway handles the rest with instant previews. Quickly provision container image services, databases, and storage volumes.
- Characterizing possible failure modes in physics-informed neural networks.☆152Nov 18, 2021Updated 4 years ago
- ☆12Oct 4, 2021Updated 5 years ago
- An automatic knowledge embedding framework for scientific machine learning☆23May 15, 2022Updated 4 years ago
- Supporting code for "Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders"☆21Jan 6, 2021Updated 5 years ago
- ☆118Feb 5, 2025Updated last year
- Ensembe PINN for improved inverse modeling☆11Apr 26, 2023Updated 3 years ago
- In computational fluid dynamics (CFD), the SIMPLE algorithm is a widely used numerical procedure to solve the Navier–Stokes equations. SI…☆16Nov 23, 2020Updated 5 years ago
- Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEs☆169May 15, 2024Updated 2 years ago
- Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems☆62Jan 25, 2022Updated 4 years ago
- Bare Metal GPUs on DigitalOcean Gradient AI • AdPurpose-built for serious AI teams training foundational models, running large-scale inference, and pushing the boundaries of what's possible.
- ☆66Mar 22, 2023Updated 3 years ago
- hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations☆96Aug 26, 2025Updated last year
- ☆29Oct 15, 2020Updated 6 years ago
- A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery.☆28Sep 30, 2021Updated 5 years ago
- Sparse Physics-based and Interpretable Neural Networks☆54Sep 30, 2021Updated 5 years ago
- TensorFlow 2.0 implementation of Yibo Yang, Paris Perdikaris’s adversarial Uncertainty Quantification in Physics Informed Neural Networks…☆22Mar 25, 2023Updated 3 years ago
- Deep residual networks for dimensionality reduction and surrogate modeling in high-dimensional inverse problems☆10Mar 20, 2021Updated 5 years ago
- Leaning Proper Orthogonal Decomposition coefficients using Deep Neural Networks.☆10Dec 4, 2019Updated 6 years ago
- Refined adaptation of Super Resolution 4D Flow MRI using Residual Neural Network☆10Feb 25, 2023Updated 3 years ago
- Managed hosting for WordPress and PHP on Cloudways • AdManaged hosting for WordPress, Magento, Laravel, or PHP apps, on multiple cloud providers. Deploy in minutes on Cloudways by DigitalOcean.
- Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonli…☆269Feb 1, 2023Updated 3 years ago
- Source code for "Probabilistic neural networks for fluid flow model-order reduction and data recovery"☆11Sep 19, 2020Updated 6 years ago
- Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels -- param…☆25Aug 2, 2021Updated 5 years ago
- ☆35Jan 29, 2021Updated 5 years ago
- ☆178Jun 27, 2022Updated 4 years ago
- Deep Learning based method to try and learn the problem of inverse Navier Stokes and model the flow for an oscillating airfoil.☆24Jun 7, 2020Updated 6 years ago
- Physics-informed learning of governing equations from scarce data☆171Jul 19, 2023Updated 3 years ago