National Strategic Computing Initiative Seminar Series
In order to maximize the benefits of HPC for economic competitiveness and scientific discovery, the United States Government must create a coordinated Federal strategy in HPC research, development, and deployment. Investment in HPC has contributed substantially to national economic prosperity and rapidly accelerated scientific discovery. Creating and deploying technology at the leading edge is vital to advancing my Administration's priorities and spurring innovation. Accordingly, this order establishes the National Strategic Computing Initiative (NSCI).
University of Iowa is participating in this seminar series via BlueJeans connection in the newly constructed Iowa Informatics Initiative Space in Public Health Building (CPHB).
The next talk in the series is by Omar Ghattas, Jackson Chair in Computational Geosciences; Professor of Geological Sciences and Mechanical Engineering: Director, Center for Computational Geosciences, Institute for Computational Engineering and Sciences, University of Texas Austin.
Title: "Extreme-scale Bayesian inverse problems, with application to flow of the Antarctic ice sheet"
Many geophysical systems are characterized by complex nonlinear behavior coupling multiple physical processes over a wide range of length and time scales. Mathematical and computational models of these systems often contain numerous uncertain parameters, making high-reliability predictive modeling a challenge. Rapidly expanding volumes of observational data--along with tremendous increases in HPC capability--present opportunities to reduce these uncertainties via solution of large-scale inverse problems. Bayesian inference provides a systematic framework for inferring model parameters with associated uncertainties from (possibly noisy) data and any prior information. However, solution of Bayesian inverse problems via conventional Markov chain Monte Carlo (MCMC) methods remains prohibitive for expensive models and high-dimensional parameters, as result from discretization of infinite dimensional problems with uncertain fields. Despite the large size of observational datasets, typically they can provide only sparse information on model parameters. Based on this property we design MCMC methods that adapt to the structure of the posterior probability and exploit an effectively-reduced parameter dimension, thereby making Bayesian inference tractable for some large-scale, high-dimensional inverse problems. We discuss inverse problems for the flow of the Antarctic ice sheet, which have been solved for as many as one million uncertain parameters at a cost (measured in forward problem solves) that is independent of the parameter dimension, the data dimension, and the number of processor cores. This work is joint with Tobin Isaac, Noemi Petra, and Georg Stadler.
Please contact Sai Ramadugu (firstname.lastname@example.org) if you plan to attend.
Tuesday, November 1, 2016 at 12:00pm to 1:00pm
College of Public Health Building, N512
145 North Riverside Drive, Iowa City, IA