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DTSTART;TZID=America/Chicago:20260730T143000
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DTSTAMP:20260728T155525Z
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SUMMARY:LANS Seminar
DESCRIPTION:Seminar Title: Digital Twins for Tsunami Early Warning: Real-Time Bayesian Inversion\, Prediction\, and OED \nSpeaker: Sreeram Venkat\, PhD candidate and NSF Graduate Research Fellow \nAffiliation: Oden Institute for Computational Engineering and Sciences\, University of Texas at Austin \n  \nDate: Thursday\, July 30\, 2026 \nTime: 2:30 PM-3:30 PM (In-Person) \nLocation: Hybrid\, Bldg. 240\, Conference Room 1404 \nHost: Srini Eswar \n  \nZoom Link: https://argonne.zoomgov.com/j/1659651870?pwd=4Sk9IBpXQ38Qy2JGz7tFAZBdPLdQcn.1 \nMeeting ID: 165 965 1870 \nPasscode: 376221 \n   \nAbstract: We address real-time Bayesian inverse problems governed by time-shift-invariant wave equations\, with particular focus on tsunami inference and optimal experimental design. Efforts are underway to instrument subduction zones with ocean bottom acoustic pressure sensors to provide tsunami early warning. Our goal is to create a physics-based early-warning system that employs this pressure data\, along with the 3D coupled acoustic–gravity wave equations\, to infer the earthquake-induced spatiotemporal seafloor motion in real time. The Bayesian solution of this inverse problem then provides the seafloor forcing to forward propagate the tsunamis toward populated areas along coastlines and issue forecasts with quantified uncertainties. In the context of the Cascadia Subduction Zone\, a single forward wave propagation requires 1 hour on a supercomputer. The Bayesian inverseproblem\, with a billion uncertain parameters\, formally requires hundreds of thousands of adjoint wave propagations; thus real time inference appears to be intractable. We propose a novel approach to enable exact solution of the inverse and prediction problems in real time. The key is to exploit the time-shift-invariance of the parameter-to-observable map\, which permits FFT diagonalization and fast GPU implementation. We demonstrate that tsunami inverse problems with a billion parameters can be solved exactly in a fraction of a second. This fast Bayesian inversion capability is then exploited to solve the optimal experimental design problem of placement of seafloor pressure sensors to maximize expected information gain in predictive quantities of interest. Time permitting\, we discuss recent work on data-driven prior construction\, goal-oriented dimension reduction\, and construction of fast surrogates for nonlinear shallow water equation-based tsunami predictions\, which are more accurate in shallower waters. This work is joint with Stefan Henneking\, Sreeram Venkat\, Bowen Shi\, and Yuhang Li at UT Austin\, and Alice Gabriel at UCSD. \n  \nBio: Sreeram Venkat is a PhD candidate and NSF Graduate Research Fellow at the Oden Institute for Computational Engineering and Sciences at the University of Texas at Austin. He is a part of the OPTIMUS group advised by Professor Omar Ghattas. Sreeram’s research interests include the design and analysis of mathematical models\, development and implementation of HPC algorithms\, and digital twins in the context of Bayesian inverse problems. Most recently\, Sreeram was part of the 2025 Gordon Bell Prize-winning team for their work on extreme-scale digital twins for tsunami early warning. Before coming to the Oden Institute\, Sreeram earned Bachelor’s degrees in Physics and Applied Mathematics from North Carolina State University. Outside of work\, Sreeram learns and performs Carnatic music at venues throughout the US and India. \n   \n  \nSee all upcoming talks at https://www.anl.gov/mcs/lans-seminars \n  \n 
URL:https://wordpress.cels.anl.gov/lans-seminars/event/lans-seminar-209/
LOCATION:Building 240 Room 1404
CATEGORIES:Seminar
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