Colloquium with Chris Rackauckas
Department of Mathematics
Massachusetts Institute of Technology

“Universal Differential Equations for Scientific Machine Learning”

Feb 19, 2020, 3:30 p.m., 499 DSL
https://arxiv.org/abs/2001.04385

Abstract:
In the context of science, the well-known adage “a picture is worth a thousand words” might well be “a model is worth a thousand datasets.” Scientific models, such as Newtonian physics or biological gene regulatory networks, are human-driven simplifications of complex phenomena that serve as surrogates for the countless experiments that validated the models. Recently, machine learning has been able to overcome the inaccuracies of approximate modeling by directly learning the entire set of nonlinear interactions from data. However, without any predetermined structure from the scientific basis behind the problem, machine learning approaches are flexible but data-expensive, requiring large databases of homogeneous labeled training data. A … READ MORE