Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning
☆97Aug 17, 2023Updated 2 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☆50Jul 23, 2020Updated 5 years ago
- Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data☆152Oct 18, 2019Updated 6 years ago
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆91Jan 24, 2021Updated 5 years ago
- POD-PINN code and manuscript☆58Nov 10, 2024Updated last year
- ☆63Jul 24, 2019Updated 6 years ago
- GPU virtual machines on DigitalOcean Gradient AI • AdGet to production fast with high-performance AMD and NVIDIA GPUs you can spin up in seconds. The definition of operational simplicity.
- Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification☆108Aug 4, 2020Updated 5 years ago
- ☆113Oct 16, 2021Updated 4 years ago
- Physics-informed neural network for solving fluid dynamics problems☆277Jan 28, 2021Updated 5 years ago
- physics-informed neural network for elastodynamics problem☆162Jan 20, 2022Updated 4 years ago
- A place to share problems solved with SciANN☆309Nov 6, 2023Updated 2 years ago
- Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs☆93May 11, 2022Updated 3 years ago
- ☆258Oct 14, 2021Updated 4 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
- Supporting code for "Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders"☆21Jan 6, 2021Updated 5 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.
- ☆26Oct 15, 2020Updated 5 years ago
- An automatic knowledge embedding framework for scientific machine learning☆23May 15, 2022Updated 3 years ago
- Characterizing possible failure modes in physics-informed neural networks.☆153Nov 18, 2021Updated 4 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
- Accelerating Physics Informed Neural Networks (PINNs) using Meshless Discretizations☆34Apr 15, 2023Updated 3 years ago
- hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations☆91Aug 26, 2025Updated 8 months ago
- Source code for POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decom…☆39Sep 7, 2023Updated 2 years ago
- Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonli…☆262Feb 1, 2023Updated 3 years ago
- Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels -- param…☆25Aug 2, 2021Updated 4 years ago
- Deploy to Railway using AI coding agents - Free Credits Offer • AdUse Claude Code, Codex, OpenCode, and more. Autonomous software development now has the infrastructure to match with Railway.
- ☆117Feb 5, 2025Updated last year
- A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery.☆27Sep 30, 2021Updated 4 years ago
- Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems☆61Jan 25, 2022Updated 4 years ago
- Leaning Proper Orthogonal Decomposition coefficients using Deep Neural Networks.☆10Dec 4, 2019Updated 6 years ago
- Deep Learning of Vortex Induced Vibrations☆100Feb 21, 2020Updated 6 years ago
- Source code for deep learning-based reduced order models in cardiac electrophysiology. Available on doi.org/10.1371/journal.pone.0239416.☆17Sep 7, 2023Updated 2 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 5 years ago
- Deep learning framework for model reduction of dynamical systems☆21Dec 31, 2020Updated 5 years ago
- ☆64Mar 22, 2023Updated 3 years ago
- Virtual machines for every use case on DigitalOcean • AdGet dependable uptime with 99.99% SLA, simple security tools, and predictable monthly pricing with DigitalOcean's virtual machines, called Droplets.
- Refined adaptation of Super Resolution 4D Flow MRI using Residual Neural Network☆10Feb 25, 2023Updated 3 years ago
- ☆36Sep 3, 2021Updated 4 years ago
- ☆24Sep 29, 2021Updated 4 years ago
- Physics-Informed Neural Networks for Cardiovascular Blood Flow Simulations☆19Apr 7, 2025Updated last year
- Multi-fidelity Generative Deep Learning Turbulent Flows☆38Jan 13, 2021Updated 5 years ago
- XPINN code written in TensorFlow 2☆28Feb 1, 2023Updated 3 years ago
- Sparse Physics-based and Interpretable Neural Networks☆53Sep 30, 2021Updated 4 years ago