Michael Glass, Nandana Mihindukulasooriya, et al.
ISWC 2017
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.
Michael Glass, Nandana Mihindukulasooriya, et al.
ISWC 2017
Conrad Albrecht, Jannik Schneider, et al.
CVPR 2025
Shashanka Ubaru, Lior Horesh, et al.
Journal of Biomedical Informatics
Advait Parulekar, Karthikeyan Shanmugam, et al.
ICML 2023