LANS Seminar

Seminar Title: Data Assimilation: Dynamical Interpolation, State Estimation, and Model
Speaker: Nan Chen
Title: Assistant Professor at the Department of Mathematics
Affiliation: University of Wisconsin-Madison

Date/Time: June 20, 2023/10:30 AM-11:30 AM
Location: See meeting URL on the cels-seminars website (requires Argonne login)

Host: Romit Maulik

Description: 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. Dr. Chen received his Ph.D. from the Courant Institute of Mathematical Sciences and the Center of Atmosphere and Ocean Science, New York University (NYU), in 2016. He worked as a postdoc research associate at NYU for two years before joining UW-Madison. Dr. Chen’s research interests lie in applied mathematics, geophysics, complex dynamical systems, stochastic methods, numerical algorithms, machine learning techniques, and general data science. He is also active in developing both dynamical and stochastic models and using these models to predict real-world phenomena related to atmosphere-ocean science, climate, and other complex systems such as the Madden-Julian Oscillation (MJO), the monsoon, the El Nino Southern Oscillation (ENSO) and the sea ice based on real observational data. His recent work also involves the development of new uncertainty quantification and stochastic methods to material science.

See all upcoming talks at https://www.anl.gov/mcs/lans-seminars

Please note that the meeting URL for this event can be seen on the cels-seminars website which requires an Argonne login.