Shape optimization in laminar flow with a label-guided variational autoencoder

Abstract

Computational design optimization in fluid dynamics usually requires to solve non-linear partial differential equations numerically. In this work, we explore a Bayesian optimization approach to minimize an object’s drag coefficient in laminar flow based on predicting drag directly from the object shape. Jointly training an architecture combining a… (More)

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Cite this paper

@inproceedings{Eismann2017ShapeOI, title={Shape optimization in laminar flow with a label-guided variational autoencoder}, author={Stephan Eismann and Stefan Bartzsch and Stefano Ermon}, year={2017} }