hinofafa / DeepPDELearner
This repository introduces Partial Differential Equation Solver using neural network that can learn resolution-invariant solution operators on Navier-Stokes equation. Solving PDE is the core subject of numerical simulation and is widely used in science and engineering, from molecular dynamics to flight simulation, and even weather forecasting.
☆16Updated 3 years ago
Alternatives and similar repositories for DeepPDELearner:
Users that are interested in DeepPDELearner are comparing it to the libraries listed below
- Python script solving the Burgers' equation (équation de Burgers) 1D by using FFT pseudo-spectral method.☆25Updated 3 years ago
- Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data☆48Updated 4 years ago
- ☆19Updated 4 years ago
- Different methods of solving partial differential equations with neural networks☆15Updated 3 years ago
- Machine learning of linear differential equations using Gaussian processes☆24Updated 6 years ago
- Tensoflow 2 implementation of physics informed deep learning.☆27Updated 4 years ago
- TensorFlow 2.0 implementation of Yibo Yang, Paris Perdikaris’s adversarial Uncertainty Quantification in Physics Informed Neural Networks…☆20Updated 2 years ago
- To address some of the failure modes in training of physics informed neural networks, a Lagrangian architecture is designed to conform to…☆47Updated 2 years ago
- ☆16Updated 7 months ago
- Solving High Dimensional Partial Differential Equations with Deep Neural Networks☆34Updated 3 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
- Source code for POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decom…☆29Updated last year
- This repository contains the files used in the paper " Reduced-order Model for Fluid Flows via Neural Ordinary Differential Equations"☆18Updated 2 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
- 🌌 Applications of Physics-Informed ML: A collection of notebooks from my Masters research, exploring how machine learning can solve scie…☆11Updated 4 months ago
- PECANNs: Physics and Equality Constrained Artificial Neural Networks☆20Updated last year
- 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 (PINNs)☆12Updated 2 years ago
- Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations☆67Updated 4 years ago
- Python codes for Locally Adaptive Activation Function (LAAF) used in deep neural networks. Please cite this work as "A D Jagtap, K Kawa…☆39Updated 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
- Multifidelity DeepONet☆30Updated last year
- ☆12Updated 10 months ago
- Bayesian optimized physics-informed neural network for parameter estimation☆25Updated 4 months ago
- Sparsity-promoting Kernel Dynamic Mode Decomposition for Nonlinear Dynamical Systems☆28Updated 2 years ago
- This repository contains the code and models for our paper "Investigating and Mitigating Failure Modes in Physics-informed Neural Network…☆18Updated last year
- PINN, DGM and DRM☆20Updated last year
- A Deep Learning based Approach to Reduced Order Modeling for Turbulent Flow Control using LSTM Neural Networks. arXiv:1804.09269☆42Updated 6 years ago
- A Backward Compatible -- Physics Informed Neural Network for Allen Cahn and Cahn Hilliard Equations☆28Updated 3 years ago
- Physics-Informed Neural Networks for solving PDEs (bachelor project)☆10Updated 2 years ago