Abstract: Data assimilation is a technique that combines models with data to improve the results. It provides initialization and is a prerequisite for predicting complex turbulent systems. In this talk, I will start by introducing the general mathematical framework of data assimilation and its computational challenges. Then I will present several aspects of scientific problems where data assimilation can play an important role. First, I will show that data assimilation can be utilized for the dynamical interpolation of missing observations of turbulent flows using statistically reduced-order models with a specific application to the Arctic Sea ice. Second, I will show that data assimilation facilitates data-driven model identification via a causality-based learning approach. If time permits, I will briefly mention combining stochastic parameterization with machine learning in data assimilation to facilitate the state estimation of complex turbulent systems.

Bio: Nan Chen is an Assistant Professor at the Department of Mathematics, University of Wisconsin-Madison. He is also a faculty affiliate of the Institute for Foundations of Data Science. Chen received his Ph.D. from the Courant Institute of Mathematical Sciences and the Center of Atmosphere and Ocean Science, New York University (NYU).