Dynamic Data-Driven Inversion for Terascale Simulations: Real-Time Identification of Airborne Contaminants Session: Large Data Applications Event Type: Paper Time: 11:00am - 11:30am Session Chair: Dave Turner Speaker(s): Volkan Akcelik, George Biros, Andrei I. Draganescu, Omar Ghattas, Judith C. Hill, Bart van Bloemen Waanders Location: 602-604 Abstract: In contrast to traditional terascale simulations that have known, fixed data inputs, dynamic data-driven (DDD) applications are characterized by unknown data and informed by dynamic observations. DDD simulations give rise to inverse problems of determining unknown data from sparse observations. The main difficulty is that the optimality system is a boundary value problem in 4D space-time, even though the forward simulation is an initial value problem. We construct special-purpose parallel multigrid algorithms that exploit the spectral structure of the inverse operator. Experiments on problems of localizing airborne contaminant release from sparse observations in a regional atmospheric transport model demonstrate that 17-million-parameter inversion can be effected in just 29 minutes on 1024 Alphaserver EV68 processors. Moreover, inverse problems with 139 billion total space-time unknowns are solved in less than 5 hours on the same number of processors. These results suggest that ultra-high resolution data-driven inversion can be carried out sufficiently rapidly for simulation-based real-time hazard assessment. This paper can be found in the ACM and IEEE Digital Libaries Click here for ACM Click here for IEEE Chair/Speaker Details: Dave Turner (Chair) Ames Laboratory Volkan Akcelik University of Texas at Austin George Biros University of Pennsylvania Andrei I. Draganescu Sandia National Labs Omar Ghattas University of Texas at Austin Judith C. Hill Sandia National Labs Bart van Bloemen Waanders Sandia National Labs