Deep Learning for Scientists and Engineers

Catalog description

The main objective of this course is to teach concepts and implementation of deep learning techniques for scientific and engineering problems to advanced undergraduate and graduate students. This course entails various methods, including theory and implementation of deep leaning techniques to solve a broad range of computational problems frequently encountered in solid mechanics, fluid mechanics, non destructive evaluation of materials, systems biology, chemistry, and non-linear dynamics. At the end of the course participants will be able to: (1) Understand the underlying theory and mathematics of deep learning; (2) Analyze and synthesize data in order to model physical, chemical, biological, and engineering systems; (3) Apply physics-informed learning to model and simulate multiphysics systems. Recommended prerequisites (not mandatory): - Linear algebra: MATH 3120 -Probability: MATH 1510/ CIS 2610 / ESE 3010 / ENM 5030 / STAT 5100 -Experience with computer programming of Python