Stanford MTT-S/APS DL Talk: Scientific Machine Learning for Electromagnetic Field Computations

Abstract:
A recent report by the US Department of Energy defines the area of scientific machine learning as
“a core component of artificial intelligence (AI) and a computational technology that can be trained, with
scientific data, to augment or automate human skills”, which has “the potential to transform science and
energy research”. In this presentation, we discuss the potential of scientific machine learning methods to
problems in computational electromagnetics starting from standard electromagnetic structure analysis
and multi-physics modeling in the time-domain, employing unsupervised, supervised and reinforcement
learning.
An unsupervised learning strategy is based on a modified Physics-Informed Neural Network (PINN). PINNs
directly integrate physical laws into their loss function, so that the training process does not rely on the
generation of training ground truth data from simulations. We introduce a physics-informed deep-
operator-network (PI-DON) for Maxwell’s equations. We show applications of the PI-DON to the analysis,
design and uncertainty quantification study of finite and defect periodic structures, including frequency
selective surfaces and metasurfaces.
Moreover, we demonstrate the impact of machine learning on the computational modeling of radiowave
propagation scenarios and reconfigurable intelligent surfaces, using supervised learning. We build
convolutional neural network models that can process the geometry of indoor environments, along with
physics-inspired parameters, to rapidly estimate received signal strength (RSS) maps. Emphasis is placed
on the generalizability of these models, which is their ability to "learn" the physics of radiowave
propagation and produce accurate modeling predictions in new geometries well beyond those included in
their training set.
Finally, the use of reinforcement learning for the optimization of reconfigurable intelligent surfaces will be
discussed.
Speaker(s): Costas
Room: 204, Bldg: David Packard Electrical Engineering Building, 350 JANE STANFORD WAY, STANFORD, California, United States, 94305