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”. We explore the potential of scientific machine learning methods to problems in computational electromagnetics starting from standard microwave structure design and multiphysics modeling, employing an unsupervised learning strategy based on Physics-Informed Neural Networks (PINN). PINNs directly integrate physical laws into their loss function, so that the training process does not rely on the generation of ground truth data from a large number of simulations (as in typical neural networks). Moreover, we demonstrate the impact of machine learning on the computational modeling of radiowave propagation scenarios. 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. We show 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. These models can be used to rapidly optimize the position of transmitters in wireless area networks, to maximize coverage or other relevant metrics. Co-sponsored by: STARaCom Speaker(s): Costas Sarris McGill Unversity, Room TBD, Montreal, Quebec, Canada