Data Science and Machine Learning in Chemical Engineering

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 neural networks (PINNs) to model and simulate multiphysics systems. Students should have prior coursework in advanced calculus, linear algebra, probability, and computer programming in Python.