An autoencoder-based bi-fidelity method utilising frozen latent spaces. Application to flow past a confined cylinder
Year: 2025
Type: article
Abstract: This study presents a simple autoencoder-based bi-fidelity method, where the latent space is constrained to be invariant between both low- and high-fidelity datasets. The method is applied to the flow past a cylinder confined in a duct, where model fidelity is governed by computational model resolution. Significant cost savings are observed without significant compromise to approximation accuracy. The influence of several hyperparameters are considered including: latent space dimension, sample number and distribution. A method of generating synthetic solutions is also explored. This is done by performing regression on the common latent space of low- and high-fidelity models, which is then passed through the high-fidelity decoder. As a large number of samples are available from the low-fidelity model, significant improvements are observed compared with the traditional method of interpolating only high-fidelity data.
Authors: W. Lu, W.K. Lam, T. Zahtila
Institutions: The University of Melbourne, Stanford University
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,420
OpenAlex ID: W4417273118