Teaching Artificial Intelligence to Chemical Engineers: Experience from a 35-year-old Course

Abstract

He considers himself an artist in science, whose natural tendency is to conduct curiosity-driven research in a style that might be considered impressionistic, emphasizing conceptual issues over mere techniques. Venkat's research interests are diverse, ranging from AI to systems engineering to theoretical physics to economics, but with a focus on understanding complexity and emergent behavior in different domains.

Shaping futures: A dialogue on chemical engineering education

“…As an educator who has taught AI in undergraduate and graduate courses for over 35 years, Venkatasubramanian explained how integrating AI modelling with first principle‐based models is imperative in teaching AI in chemical engineering. [ 69 ] The mechanistic understanding based on first principles in physics, chemistry, and/or biology of our systems must govern any models that lead to hybrid AI models. Models based on symbolic AI (i.e., the methods of knowledge‐based expert systems) can deal with complex and nonlinear systems, often seen in industrial processes, with incomplete and/or uncertain data.…”