DeepAI AI Chat
Log In Sign Up

A Comparison of Neural Network Architectures for Data-Driven Reduced-Order Modeling

by   Anthony Gruber, et al.
Florida State University
University of South Carolina

The popularity of deep convolutional autoencoders (CAEs) has engendered effective reduced-order models (ROMs) for the simulation of large-scale dynamical systems. However, it is not known whether deep CAEs provide superior performance in all ROM scenarios. To elucidate this, the effect of autoencoder architecture on its associated ROM is studied through the comparison of deep CAEs against two alternatives: a simple fully connected autoencoder, and a novel graph convolutional autoencoder. Through benchmark experiments, it is shown that the superior autoencoder architecture for a given ROM application is highly dependent on the size of the latent space and the structure of the snapshot data, with the proposed architecture demonstrating benefits on data with irregular connectivity when the latent space is sufficiently large.


page 8

page 17

page 20

page 21

page 22


Drum Beats and Where To Find Them: Sampling Drum Patterns from a Latent Space

This paper presents a large dataset of drum patterns and compares two di...

gLaSDI: Parametric Physics-informed Greedy Latent Space Dynamics Identification

A parametric adaptive physics-informed greedy Latent Space Dynamics Iden...

Neural Network Surrogate Models for Absorptivity and Emissivity Spectra of Multiple Elements

Simulations of high energy density physics are expensive in terms of com...

Connectivity-Optimized Representation Learning via Persistent Homology

We study the problem of learning representations with controllable conne...

Reduced order modeling for flow and transport problems with Barlow Twins self-supervised learning

We propose a unified data-driven reduced order model (ROM) that bridges ...

Deep Convolutional Autoencoders as Generic Feature Extractors in Seismological Applications

The idea of using a deep autoencoder to encode seismic waveform features...