Finding a reasonable hypothesis can pose a challenge when there are thousands of possibilities. This is why Dr. Joseph Sang-II Kwon is trying to make hypotheses in a generalizable and systematic manner.
Kwon, an associate professor in the Artie McFerrin Department of Chemical Engineering at Texas A&M University, published his work on blending traditional physics-based scientific models with experimental data to accurately predict hypotheses in the journal Nature Chemical Engineering.
Kwon’s research extends beyond the realm of traditional chemical engineering. By connecting physical laws with machine learning, his work could impact renewable energy, smart manufacturing, and health care, outlined in his recent paper, “Adding big data into the equation.”
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