Surrogate 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 ...Surrogate 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.展开更多
基金This work was supported in part by IBM Research,the MIT-IBM Watson AI Laboratory,the U.S.Army Research Office through the Institute for Soldier Nanotechnologies(under award W911NF-13-D-0001)by the PAPPA program of DARPA MTO(under award HR0011-20-90016).
文摘Surrogate 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.
