연세대학교 계산과학공학

2026.06.18.(목)  17:00

첨단관 615호

Joseph S. Kwon (Richard Morrow Endowed Chair and Professor, Chemical and Biomolecular Engineering at The Ohio State University)

Hybrid Modeling of Chemical Processes: Integrating Physics with Machine Learning to Capture Missing Physics

Traditional dynamic modeling of chemical processes relies on first-principles models grounded in the fundamental laws of physics and chemistry. These models, primarily formulated as differential equations with constant parameters, enable the calculation of control actions that optimize process operations while accounting for both process and actuator limitations. Although we possess substantial knowledge of most chemical process systems, it is difficult to claim perfect knowledge. To address this gap, we have developed a hybrid modeling framework that combines system-agonistic first-principles dynamics with a machine learning model (e.g., a transformer model, which has been a cornerstone of ChatGPT’s success) to capture the missing physics. This framework is applicable across modeling types (e.g., DFT, MD, kMC, and CFD) and scales (from lab to plant), and is currently being demonstrated in partnership with leading chemical process industries.

BIO: Joseph S. Kwon is the Richard Morrow Endowed Chair and Professor in Chemical and Biomolecular Engineering at The Ohio State University. Prior to this, he served as Assistant and Associate Professor at Texas A&M University, where he held the Kenneth R. Hall Career Development Professorship. He holds degrees in Mathematics and Chemical Engineering (B.S., University of Minnesota), Electrical Engineering (M.S., University of Pennsylvania), and Chemical Engineering (Ph.D., UCLA). Dr. Kwon's research lies in Process Systems Engineering, with a focus on multiscale and hybrid modeling, model predictive control, and optimization. He has published over 170 papers, delivered numerous invited talks at industry and academic institutions, and secured over $10M in research funding. His work has been recognized with multiple national and institutional awards, including those from NSF, DOE, KIChE, KSEA, and TAMU.

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