Fundamentals of Machine Learning for Two-Phase Heat Transfer

Multiphase phenomena play an integral role in both nature and industry, impacting everything from dew condensation on insects to water harvesting, electronics cooling, climate modeling, hydrogen production, and manufacturing. Despite over a century of study, key scientific questions remain about the mechanisms behind these complex processes. The interaction between phase boundary evolution and mass transport leads to nonlinear behaviors, where even small parameter shifts can have significant, unexpected impacts. These processes are multimodal, multidimensional, and transient, posing major challenges for both investigation and understanding. Furthermore, interpreting experimental data and predicting multiphase behaviors continue to be complex tasks. To address these challenges, my research group integrates advanced computer vision and machine learning techniques. In this talk, I will showcase several of our key approaches that reveal previously unrecognized features and hidden mechanisms in multiphase flows, demonstrating how AI technologies can enhance our ability to learn, understand, and predict their dynamic nature. I will then discuss how incorporating physics-based priors can improve machine learning based predictive models, making them more generalizable and explainable. Finally, I will discuss potential game-changing innovations that these approaches could bring to the thermofluidic domain.

Horea Ilies headshotDr. Yoonjin Won, University of Califonria, Irvine 

Dr. Yoonjin Won is currently an Associate Professor of Mechanical and Aerospace Engineering at the University of California, Irvine, with courtesy appointments in Electrical Engineering and Computer Science, and Materials Science Engineering. Dr. Won's research focuses on multiphase thermal science, integrating AI for science and experiment, scientific machine learning, and materials design. She leads the DoD funded multi-university research initiative (MURI), ML4Heat. She is a recipient of the National Science Foundation CAREER Award, the ASME Electronic & Photonic Packaging Division Early Career Award, the ASME Electronic & Photonic Packaging Division Women Engineer Award, the ASME ICNMM Outstanding Leadership Award, the Emerging Innovation/Early Career Innovator from UCI Beall Innovation Center, Faculty Excellence in Re- search Awards (Mid-Career) from UCI, and numerous best paper and poster awards. Yoonjin Won received her B.S. degree in Mechanical and Aerospace Engineering from Seoul National University, and her M.S. and Ph.D. degrees in Mechanical Engineering from Stanford University. For more information on Dr. Won’s qualifications and research group, please visit won.eng.uci.edu.