A comparison of neural network architectures for data-driven reduced-order modeling
The popularity of deep convolutional autoencoders (CAEs) has engendered new and effective reduced-order models (ROMs) for the simulation of large-scale dynamical systems. Despite this, it is still unknown whether deep CAEs provide superior performance over established linear techniques or other network-based methods in all modeling 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.
- Research Organization:
- Univ. of South Carolina, Columbia, SC (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Advanced ScientificComputing Research (ASCR). Scientific Discovery through Advanced Computing (SciDAC)
- Grant/Contract Number:
- SC0020270; SC0020418
- OSTI ID:
- 1865639
- Journal Information:
- Computer Methods in Applied Mechanics and Engineering, Journal Name: Computer Methods in Applied Mechanics and Engineering Vol. 393; ISSN 0045-7825
- Publisher:
- ElsevierCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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