youngkyu-kim / Gappy-AELinks
☆13Updated last year
Alternatives and similar repositories for Gappy-AE
Users that are interested in Gappy-AE are comparing it to the libraries listed below
Sorting:
- Prediction of Fluid Flow in Porous Media by Sparse Observations and Physics-Informed PointNet☆13Updated last year
- Code for "Robust flow field reconstruction from limited measurements vis sparse representation" (J. Callaham, K. Maeda, and S. Brunton 20…☆14Updated 6 years ago
- Physics-informed radial basis network☆32Updated last year
- Reduced-Order Modeling of Fluid Flows with Transformers☆24Updated 2 years ago
- ☆32Updated 3 years ago
- Source code for deep learning-based reduced order models for nonlinear time-dependent parametrized PDEs. Available on doi.org/10.1007/s10…☆25Updated 2 years ago
- Effective data sampling strategies and boundary condition constraints of physics-informed neural networks for identifying material proper…☆21Updated 2 years ago
- XPINN code written in TensorFlow 2☆28Updated 2 years ago
- ☆41Updated 3 years ago
- ☆34Updated 4 years ago
- ☆12Updated last year
- Accelerating Physics Informed Neural Networks (PINNs) using Meshless Discretizations☆28Updated 2 years ago
- Discontinuity Computing Using Physics-Informed Neural Network☆25Updated last year
- PINNs-MPF is a comprehensive framework designed for simulating interface dynamics using Physics-Informed Neural Networks (PINNs). Leverag…☆16Updated 5 months ago
- Source code for POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decom…☆32Updated 2 years ago
- Multi-fidelity reduced-order surrogate modeling☆25Updated 3 months ago
- The MegaFlow2D dataset package☆23Updated last year
- GCA-ROM is a library which implements graph convolutional autoencoder architecture as a nonlinear model order reduction strategy.☆36Updated this week
- Implementation of 'Physics-Informed Neural Networks for Shell Structures' (European Journal of Mechanics A)☆43Updated last year
- Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of n…☆35Updated 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
- Finite Volume PINNs for Hyperbolic Conservation Laws & Compressible Flow☆19Updated 2 years ago
- Soving heat transfer problems using PINN with tf2.0☆19Updated 4 years ago
- This repository includes the implementation of the Physics Informed Neural Network and The Deep Energy Method on 1D, 2D boundary value an…☆15Updated 3 years ago
- Deep Learning based method to try and learn the problem of inverse Navier Stokes and model the flow for an oscillating airfoil.☆22Updated 5 years ago
- Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems☆56Updated 3 years ago
- Coupled-Automatic-Numerical differentiation scheme for physics-informed neural network (can-PINN)☆32Updated last year
- The repository contains implementations of examples provided in the literature on energy minimization based approach to Physics Informed …☆11Updated 5 years ago
- ☆12Updated 2 months ago
- Neural operator learning of heterogeneous mechanobiological insults contributing to aortic aneurysms☆11Updated 10 months ago