POD-Galerkin reduced order models and physics-informed neural networks for solving inverse problems for the Navier–Stokes equations

POD-Galerkin reduced order models and physics-informed neural networks for solving inverse problems for the Navier–Stokes equations

Abstract We present a Reduced Order Model (ROM) which exploits recent developments in Physics Informed Neural Networks (PINNs) for solving inverse problems for the Navier–Stokes equations (NSE). In the proposed approach, the presence of simulated data for the fluid dynamics fields is assumed. A POD-...

Full description

Bibliographic Details
Main Authors: Saddam Hijazi, Melina Freitag, Niels Landwehr
Format: Article
Language:English
Published: SpringerOpen 2023-03-01
Series:Advanced Modeling and Simulation in Engineering Sciences
Subjects:
Proper orthogonal decomposition
Inverse problems
Physics-based machine learning
Navier–Stokes equations
Online Access:https://doi.org/10.1186/s40323-023-00242-2

Internet

https://doi.org/10.1186/s40323-023-00242-2

Similar Items