Explores the application of machine learning, particularly deep learning, in advancing the scientific principles for engineering complex systems, a field known as Scientific Machine Learning, or SciML. Discusses methodologies for addressing challenges in computational mechanics, mechanical design, and fluid dynamics. Examines approaches to create domain-aware, interpretable, and robust SciML algorithms to overcome the limitations of purely data-driven methods.
Offering history
Term
Sections
Enrolled
Capacity
Full
Open seats/section
Fall 2025
1
11
25
44%
14.0
Snapshots from scheduled scrapes — not live seat availability. "Full" can exceed 100% when sections over-enroll.
Meeting times
Share of recent sections by weekday: M 100% · T 0% · W 0% · Th 100% · F 0%
Common patterns: MR (100% of sections)
— in patterns, R means Thursday