Skip to content

Source code for POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition. Available on doi.org/10.1016/j.cma.2021.114181.

Notifications You must be signed in to change notification settings

stefaniafresca/POD-DL-ROM

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

6 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

POD-DL-ROM

Source code for POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition.

Abstract

Deep learning-based reduced order models (DL-ROMs) have been recently proposed to overcome common limitations shared by conventional reduced order models (ROMs) – built, e.g., through proper orthogonal decomposition (POD) – when applied to nonlinear time-dependent parametrized partial differential equations (PDEs). These might be related to (i) the need to deal with projections onto high dimensional linear approximating trial manifolds, (ii) expensive hyper-reduction strategies, or (iii) the intrinsic difficulty to handle physical complexity with a linear superimposition of modes. All these aspects are avoided when employing DL-ROMs, which learn in a non-intrusive way both the nonlinear trial manifold and the reduced dynamics, by relying on deep (e.g., feedforward, convolutional, autoencoder) neural networks. Although extremely efficient at testing time, when evaluating the PDE solution for any new testing-parameter instance, DL-ROMs require an expensive training stage, because of the extremely large number of network parameters to be estimated. In this paper we propose a possible way to avoid an expensive training stage of DL-ROMs, by (i) performing a prior dimensionality reduction through POD, and (ii) relying on a multi-fidelity pretraining stage, where different physical models can be efficiently combined. The proposed POD-DL-ROM is tested on several (both scalar and vector, linear and nonlinear) time-dependent parametrized PDEs (such as, e.g., linear advection–diffusion–reaction, nonlinear diffusion–reaction, nonlinear elastodynamics, and Navier–Stokes equations) to show the generality of this approach and its remarkable computational savings.

Citation

@article{FRESCA2022114181,
title = {POD-DL-ROM: Enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition},
journal = {Computer Methods in Applied Mechanics and Engineering},
volume = {388},
pages = {114181},
year = {2022},
issn = {0045-7825},
doi = {https://doi.org/10.1016/j.cma.2021.114181},
url = {https://www.sciencedirect.com/science/article/pii/S0045782521005120},
author = {Stefania Fresca and Andrea Manzoni},
keywords = {Reduced order modeling, Deep learning, Proper orthogonal decomposition, Dimensionality reduction, Parametrized PDEs}}

For the full implementation of the POD-DL-ROM neural network, please write to stefania.fresca@polimi.it.

About

Source code for POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition. Available on doi.org/10.1016/j.cma.2021.114181.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages