Mini-symposium (MS)

MS1:  Reduced Order Modeling in the Age of Data

Organizers: Traian Iliescu, Virginia Tech

                    Alessandro Veneziani, Emory University

                    Omer San, Oklahoma State University

Abstract: This mini-symposium aims at giving a survey of recent developments in reduced order modeling, with a special emphasis on data-driven modeling and machine learning.  Computational modeling, numerical analysis, and applications to realistic engineering, geophysical, and biomedical problems will be covered in this mini-symposium. Both achievements and open problems in reduced order modeling will be discussed.

Saturday, September 18, 10:00 AM – 12:30 PM: Part I of III

Room: Libry 3027

10:00 – 10:30 Changhong Mou, Virginia Tech, Data-driven variational multiscale reduced order models     

10:30 – 11:00 Fei Lu, Johns Hopkins University, Stochastic closure model via parametric inference

11:00 – 11:30 Honghu Liu, Virginia Tech, Dynamics informed data-driven closures for chaotic systems

11:30 – 12:00 Leo Rebholz, Clemson University, Ill-effects of model inconsistency in reduced order models of incompressible flows

12:00 – 12:30 Maxim Olshanskii, University of Houston, Interpolatory tensorial reduced-order models for parametric dynamical systems

Saturday, September 18, 3:30 PM – 5:30 PM: Part II of III

Room: Libry 3027

3:30 – 4:00 Shady E Ahmed, Oklahoma State University, A hybrid variational multiscale and machine learning approach for nonlinear model order reduction

4:00 – 4:30 Romit Maulik, Argonne National Laboratory, Modified neural ordinary differential equations for stable learning of chaotic dynamics

4:30 – 5:00 Suraj Pawar, Oklahoma State University, Physics-guided machine learning for projection-based reduced-order modeling       

5:00 – 5:30 Rohit Vuppala, Oklahoma State University, Data-driven realistic wind data generation for safe operation of Small Unmanned Air Vehicles in urban environment

Sunday, September 19, 10:30 AM – 12:30 PM: Part III of III

Room: Libry 3027

10:30 – 11:00 Alessandro Veneziani, Emory University, Some applications of model reduction in cardiovascular mathematics

11:00 – 11:30 Nan Chen, University of Wisconsin-Madison, An efficient reduced order modeling method for Lagrangian data assimilation of turbulent flows

11:30 – 12:00 Mauro Perego, Sandia National Laboratory, Modeling land ice with deep operator networks

12:00 – 12:30 Daniele Schiavazzi, University of Notre Dame, Adaptive surrogate modeling for variational inference with normalizing flow