CEE 598 PIO

Subject offerings of new and developing areas of knowledge in civil and environmental engineering intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. Course Information: May be repeated in the same or separate terms if topics vary.

Physics-Informed Machine Learning for Water and Environmental Engineering This course covers several areas of scientific machine learning (ML), including how to (a) construct data-driven ML models, (b) constrain ML models with the laws of physics and engineering principles, and (c) apply physics-informed ML models to engineering problems. The main topics include physics-informed Deep Neural Networks, Gaussian process regression, and theory-guided machine learning. The focus is on flow, transport, and other processes key to water resources and civil and environmental engineering applications.