mroberto166 / PinnsSub
☆64Updated last year
Alternatives and similar repositories for PinnsSub:
Users that are interested in PinnsSub are comparing it to the libraries listed below
- Proper Orthogonal Decomposition - Radial Basis Function (POD-RBF) Network☆65Updated last year
- POD-PINN code and manuscript☆51Updated 5 months ago
- Physics Informed Neural Networks (based on Raissi et al) extended to three dimensions on the heat diffusion equations☆17Updated 2 years ago
- Multi-fidelity reduced-order surrogate modeling☆21Updated 4 months ago
- hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations☆77Updated 2 years ago
- Sparse Physics-based and Interpretable Neural Networks☆48Updated 3 years ago
- ☆101Updated last year
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆84Updated 4 years ago
- We propose a conservative physics-informed neural network (cPINN) on decompose domains for nonlinear conservation laws. The conservation …☆70Updated 2 years ago
- Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs☆92Updated 2 years ago
- Implementation of PINNs in TensorFlow 2☆76Updated last year
- GCA-ROM is a library which implements graph convolutional autoencoder architecture as a nonlinear model order reduction strategy.☆36Updated 2 weeks ago
- XPINN code written in TensorFlow 2☆27Updated 2 years ago
- Deep learning library for solving differential equations on top of PyTorch.☆61Updated 4 years ago
- ☆116Updated 5 years ago
- Here I will try to implement the solution of PDEs using PINN on pytorch for educational purpose☆48Updated 2 years ago
- Easy Reduced Basis method☆84Updated last month
- Deep Learning for Reduced Order Modelling☆97Updated 3 years ago
- A Python package for spectral proper orthogonal decomposition (SPOD).☆108Updated 5 months ago
- Implementation of Physics-Informed Neural Networks for Computational Mechanics based on the DeepXDE package.☆36Updated last week
- Theory-guided physics-informed neural networks for boundary layer problems with singular perturbation☆17Updated 2 years ago
- Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning☆87Updated last year
- Transformed Generative Pre-Trained Physics-Informed Neural Networks (TGPT-PINN), a framework that extends Physics-Informed Neural Network…☆15Updated last year
- Source code for POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decom…☆29Updated last year
- Physics Informed Neural Network (PINN) for Burgers' equation.☆70Updated 8 months ago
- ☆92Updated 3 years ago
- A Backward Compatible -- Physics Informed Neural Network for Allen Cahn and Cahn Hilliard Equations☆29Updated 3 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
- Physics Informed Neural Networks: a starting step for CFD specialists☆30Updated 2 years ago
- Deep Learning of Vortex Induced Vibrations☆93Updated 5 years ago