mroberto166 / PinnsSubLinks
☆70Updated last year
Alternatives and similar repositories for PinnsSub
Users that are interested in PinnsSub are comparing it to the libraries listed below
Sorting:
- Proper Orthogonal Decomposition - Radial Basis Function (POD-RBF) Network☆68Updated last year
- ☆103Updated last year
- hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations☆81Updated this week
- Sparse Physics-based and Interpretable Neural Networks☆51Updated 3 years ago
- Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning☆89Updated 2 years ago
- Deep Learning of Vortex Induced Vibrations☆98Updated 5 years ago
- code☆15Updated last year
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆87Updated 4 years ago
- A Python package for spectral proper orthogonal decomposition (SPOD).☆109Updated this week
- POD-PINN code and manuscript☆52Updated 9 months ago
- We propose a conservative physics-informed neural network (cPINN) on decompose domains for nonlinear conservation laws. The conservation …☆72Updated 2 years ago
- Deep Learning for Reduced Order Modelling☆100Updated 3 years ago
- Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs☆93Updated 3 years ago
- Easy Reduced Basis method☆86Updated last month
- Data-driven Reynolds stress modeling with physics-informed machine learning☆93Updated 6 years ago
- ☆74Updated 9 months ago
- A pytorch implementation of several approaches using PINN to slove turbulent flow☆79Updated last year
- Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonli…☆207Updated 2 years ago
- Basic implementation of physics-informed neural network with pytorch.☆77Updated 2 years ago
- ODIL (Optimizing a Discrete Loss) is a Python framework for solving inverse and data assimilation problems for partial differential equat…☆114Updated 3 weeks ago
- Physics informed neural network (PINN) for cavity flow governed by Navier-Stokes equation.☆151Updated 5 years ago
- Solving PDEs with NNs☆55Updated 2 years ago
- Discontinuity Computing Using Physics-Informed Neural Network☆24Updated 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
- Source code for POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decom…☆32Updated last year
- Physics Informed Neural Network (PINN) for Burgers' equation.☆71Updated last year
- Physics-informed neural networks for highly compressible flows 🧠🌊☆27Updated last year
- Using Physics-Informed Deep Learning (PIDL) techniques (W-PINNs-DE & W-PINNs) to solve forward and inverse hydrodynamic shock-tube proble…☆184Updated 2 years ago
- POD and DMD decomposition of data from fluid dynamics. This work has been produced during my internship at the von Karman Institute for F…☆32Updated 4 years ago
- hPINN: Physics-informed neural networks with hard constraints☆141Updated 3 years ago