Soohwan Hwang

Soohwan Hwang works on engineering problems involving particles and fluids, such as how solid particles move and interact within gases or liquids in industrial equipment. Recent work combines machine learning (deep learning and recurrent neural networks) with physical modeling to predict particle behavior, detect equipment faults, and improve measurement techniques in systems like chemical looping reactors and slurry bubble columns.

Publication output has been low but has grown modestly over the past decade, rising from no output before 2019 to a peak of 3 papers in 2022, with roughly 1-2 papers per year since.

Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026