Seminar: New Solution Paradigms for Uncertainty Forecasting in High-Dimensional Nonlinear Stochastic Systems
In this talk, we will look at a snapshot of the new Laboratory for Autonomy in Data Driven and Complex Systems (LADDCS) at the Aerospace Research Center (ARC). LADDCS focuses on theoretical and computational research in uncertainty characterization, forecasting and fusion, as well as control of uncertain (better known as stochastic) systems. Challenges associated with the Fokker-Planck equation (FPE), a holy grail problem in stochastic dynamics will be discussed. Two fundamentally distinct solution paradigms will be presented – i.) tensor-based discretization, and, ii.) adaptive particle discretization in the framework of Monte Carlo methods. The key objective of both classes of methods is to accurately capture non-Gaussianity while alleviating the curse of dimensionality. We will present our results in a wide variety of fields, including weather-forecasting, space-situational awareness and the modeling of polymeric fluids.
About the Speaker
Dr. Mrinal Kumar received a Ph.D. in 2009 from Texas A&M University and a Bachelor’s degree in 2004 from the Indian Institute of Technology, Kanpur, both in aerospace engineering. Before joining OSU in Fall 2016 as an Associate Professor in the MAE department, he was an Assistant Professor in the same department at the University of Florida. At OSU, Dr. Kumar founded the Laboratory for Autonomy in Data-Driven and Complex Systems (LADDCS), which is home to research in nonlinear stochastic dynamical systems, Fokker‐Planck equations, Markov‐chain Monte Carlo methods, stochastic optimization and chance-constrained optimization and control. He received the Best Paper of Conference Award at the 2006 Astrodynamics Specialist Conference, the 2007 AIAA Open Topic Graduate Research Award, and more recently, the NSF CAREER Award in 2013 and the AFOSR Young Investigator Award in 2015.