Applied Mathematics Colloquium - Nathan Kutz
Nathan Kutz, Department of Applied Mathematics, University of Washington
The Future of Governing Equations
Machine learning and AI algorithms are transforming a diverse number of fields in science and engineering. This is largely due their success inÌýmodel discovery which turns data into reduced order 91ÃÛÌÒ¸ó and neural network representations that are not just predictive, but provide insight into the nature of the underlying dynamical system that generated the data. We introduce a number of data-driven strategies for discovering nonlinear multiscale dynamical systems, compact representations, and their embeddings from data. Importantly, data-driven architectures must jointly discover coordinates and parsimonious 91ÃÛÌÒ¸ó in order to produce maximally generalizable and interpretable 91ÃÛÌÒ¸ó of physics-basedÌýsystems and processes.