ICES and others awarded $12.5 million grant to establish center for applied mathematics research

[[A multi-institutional team of researchers led by The University of Texas at Austin’s Omar Ghattas and Karen Willcox of the Massachusetts Institute of Technology has been awarded a five-year, $12.5 million grant by the U.S. Department of Energy's Advanced Scientific Computing Research program to create the "DiaMonD" Center for applied mathematics research at the interfaces of data, models and decision-making. Read more. ]]

Co-principal investigators from UT include George Biros, Clint Dawson, Robert Moser and J. Tinsley Oden, while senior personnel include Tan Bui-Thanh, Craig Michoski, Noemi Petra, Georg Stadler, and Hari Sundar. Other institutions involved include MIT, Florida State, Colorado State, Stanford, Los Alamos National Lab, and Oak Ridge National Lab.

The DiaMonD Center aims to address the challenges of end-to-end, data-to-decisions modeling and simulation for complex problems in computational science and engineering in a unified and integrated way.

The goals of DiaMonD are:

(1) To develop advanced mathematical methods for multiphysics and multiscale problems driven by frontier U.S. Department of Energy applications, including those in subsurface energy and environmental flows, materials for energy storage and conversion, and climate systems.

(2) To create theory and algorithms for integrated inversion, optimization, and uncertainty quantification (UQ) for these complex problems.

(3) To disseminate the center's "data-to-decisions" approach to the broader applied math and computational science communities through workshops and other forms of outreach.

"Addressing end-to-end data-to-decisions modeling and simulation presents mathematical challenges of the highest order," said Omar Ghattas, a professor in the departments of geological sciences and mechanical engineering and UT’s Institute for Computational Engineering and Sciences.

"Given data, how do we infer complex models with associated uncertainty? How do we design new experiments to reduce the uncertainties in these models? And finally how do we optimally design and control systems governed by these models? Carrying out these tasks for the large, complex models that characterize leading edge DOE applications has been intractable historically."

"Research on forward, inverse, optimization, and UQ problems has all-too-often progressed in isolation," said Karen Willcox, a professor and associate department head of aeronautics and astronautics and co-director of the Center for Computational Engineering at MIT.

"This has led to mathematical methods that perform well in isolation, but are prohibitive or suboptimal or unstable when combined with other methods within the framework of inversion, optimization, or UQ. The reverse is also true: general-purpose methods developed within the inversion, optimization, and UQ fields often become prohibitive when applied to complex models since they do not exploit model structure."