Active learning of deep surrogates for PDEs: application to metasurface design

Author:

Pestourie RaphaëlORCID,Mroueh Youssef,Nguyen Thanh V.,Das PayelORCID,Johnson Steven G.

Abstract

AbstractSurrogate models for partial differential equations are widely used in the design of metamaterials to rapidly evaluate the behavior of composable components. However, the training cost of accurate surrogates by machine learning can rapidly increase with the number of variables. For photonic-device models, we find that this training becomes especially challenging as design regions grow larger than the optical wavelength. We present an active-learning algorithm that reduces the number of simulations required by more than an order of magnitude for an NN surrogate model of optical-surface components compared to uniform random samples. Results show that the surrogate evaluation is over two orders of magnitude faster than a direct solve, and we demonstrate how this can be exploited to accelerate large-scale engineering optimization.

Funder

United States Department of Defense | Defense Advanced Research Projects Agency

United States Department of Defense | U.S. Army

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Mechanics of Materials,General Materials Science,Modeling and Simulation

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