barbagroup / jcs_paper_pinn
PINN paper that will be submitted to Journal of Computational Science
☆11Updated 9 months ago
Alternatives and similar repositories for jcs_paper_pinn:
Users that are interested in jcs_paper_pinn are comparing it to the libraries listed below
- Source code for deep learning-based reduced order models in cardiac electrophysiology. Available on doi.org/10.1371/journal.pone.0239416.☆15Updated last year
- Physics-Informed Neural Networks for solving PDEs (bachelor project)☆10Updated 2 years ago
- Implementation of a Physics Informed Neural Network (PINN) written in Tensorflow v2, which is capable of solving Partial Differential Equ…☆14Updated 3 years ago
- This codes calculates the dimensionalized POD and uses SINDy from the PySINDy python package to build a data-driven model for it. The cod…☆19Updated 4 years ago
- Sparse Identification of Nonlinear Dynamics for Boundary Value Problems☆12Updated 4 years ago
- Source code for deep learning-based reduced order models for nonlinear time-dependent parametrized PDEs. Available on doi.org/10.1007/s10…☆24Updated last year
- ☆10Updated last year
- The unsupervised learning problem trains a diffeomorphic spatio-temporal grid, that registers the output sequence of the PDEs onto a non-…☆19Updated 2 years ago
- Sparse Identification of Truncation Errors (SITE) for Data-Driven Discovery of Modified Differential Equations☆10Updated 5 years ago
- TensorFlow 2.0 implementation of Yibo Yang, Paris Perdikaris’s adversarial Uncertainty Quantification in Physics Informed Neural Networks…☆20Updated 2 years ago
- Sample codes of CNN-SINDy based reduced-order modeling for fluid flows by Fukami et al., JFM 2021.☆25Updated 3 years ago
- Deep Learning of Turbulent Scalar Mixing☆17Updated 5 years ago
- Nonlinear proper orthogonal decomposition for convection-dominated flows☆13Updated 3 years ago
- Source code for POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decom…☆30Updated last year
- Theory-guided physics-informed neural networks for boundary layer problems with singular perturbation☆18Updated 2 years ago
- The lid-driven cavity is a popular problem within the field of computational fluid dynamics (CFD) for validating computational methods. I…☆14Updated 3 years ago
- Data-driven Identification of 2D Partial Differential Equations using Extracted Physical Features☆11Updated 4 years ago
- Physics-informed neural networks (PINNs)☆12Updated 2 years ago
- Finite Volume PINNs for Hyperbolic Conservation Laws & Compressible Flow☆18Updated 2 years ago
- Source code for the paper "Data-driven reduced-order models via regularised Operator Inference for a single-injector combustion process" …☆30Updated 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.☆20Updated 4 years ago
- XPINN code written in TensorFlow 2☆27Updated 2 years ago
- Variational Physic-informed Neural Operator (VINO) for Learning Partial Differential Equations☆13Updated this week
- ☆16Updated 9 months ago
- Dimensionless learning codes for our paper called "Data-driven discovery of dimensionless numbers and governing laws from scarce measurem…☆37Updated 11 months ago
- Multifidelity DeepONet☆32Updated last year
- ☆34Updated 2 weeks ago
- Python script solving the Burgers' equation (équation de Burgers) 1D by using FFT pseudo-spectral method.☆26Updated 3 years ago
- ITHACA-SEM - In real Time Highly Advanced Computational Applications for Spectral Element Methods☆20Updated 2 years ago
- ☆17Updated 4 years ago