kochlisGit / Physics-Informed-Neural-Network-PINN-Tensorflow
Implementation of a Physics Informed Neural Network (PINN) written in Tensorflow v2, which is capable of solving Partial Differential Equations.
β14Updated 2 years ago
Alternatives and similar repositories for Physics-Informed-Neural-Network-PINN-Tensorflow:
Users that are interested in Physics-Informed-Neural-Network-PINN-Tensorflow are comparing it to the libraries listed below
- Source code for POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomβ¦β30Updated last year
- Physics-informed neural networks for highly compressible flows π§ πβ26Updated last year
- Implementing physics informed neural networks (PINN) in PyTorch to solve turbulent flows using the Navier-Stokes equationsβ21Updated 11 months ago
- Theory-guided physics-informed neural networks for boundary layer problems with singular perturbationβ17Updated 2 years ago
- Physics-informed convolutional-recurrent neural networks for solving spatiotemporal PDEsβ31Updated 3 years ago
- Discontinuity Computing Using Physics-Informed Neural Networkβ23Updated last year
- Yet another PINN implementationβ20Updated 10 months ago
- To address some of the failure modes in training of physics informed neural networks, a Lagrangian architecture is designed to conform toβ¦β47Updated 2 years ago
- Source code for the paper "Data-driven reduced-order models via regularised Operator Inference for a single-injector combustion process" β¦β30Updated 2 years ago
- Source code for deep learning-based reduced order models for nonlinear time-dependent parametrized PDEs. Available on doi.org/10.1007/s10β¦β24Updated last year
- Generative Adversarial Networks are used to super resolve turbulent flow fields from low resolution (RANS/LES) fields to high resolution β¦β23Updated 4 years ago
- Sample codes of CNN-SINDy based reduced-order modeling for fluid flows by Fukami et al., JFM 2021.β25Updated 3 years ago
- Source code for deep learning-based reduced order models in cardiac electrophysiology. Available on doi.org/10.1371/journal.pone.0239416.β15Updated last year
- Sparse Identification of Nonlinear Dynamics for Boundary Value Problemsβ12Updated 4 years ago
- PINNs for 2D Incompressible Navier-Stokes Equationβ44Updated 11 months ago
- β16Updated 6 months ago
- Physics-Informed Neural Networks for solving PDEs (bachelor project)β10Updated 2 years ago
- The unsupervised learning problem trains a diffeomorphic spatio-temporal grid, that registers the output sequence of the PDEs onto a non-β¦β19Updated 2 years ago
- Proper Orthogonal Decomposition - Radial Basis Function (POD-RBF) Networkβ65Updated last year
- Finite Volume PINNs for Hyperbolic Conservation Laws & Compressible Flowβ18Updated 2 years ago
- Multifidelity DeepONetβ31Updated last year
- This codes calculates the dimensionalized POD and uses SINDy from the PySINDy python package to build a data-driven model for it. The codβ¦β19Updated 3 years ago
- β9Updated last year
- β33Updated 2 weeks ago
- β37Updated 2 years ago
- parallel PINNs; RANS equations; spatiotemporal parallel; PINNsβ31Updated last year
- combination of sparse identification of nonlinear dynamics with Akaike information criteriaβ16Updated 7 years ago
- This repository contains the files used in the paper " Reduced-order Model for Fluid Flows via Neural Ordinary Differential Equations"β19Updated 2 years ago
- Transformed Generative Pre-Trained Physics-Informed Neural Networks (TGPT-PINN), a framework that extends Physics-Informed Neural Networkβ¦β15Updated last year
- β24Updated 3 months ago