Venue: 1008 FXB
Bio: Michael Eldred is a Distinguished Member of the Technical Staff in the Optimization and Uncertainty Quantification Department within the Computation, Computers, Information, and Mathematics Center at Sandia National Laboratories. He received his B.S. in Aerospace Engineering from Virginia Tech in 1989, his M.S.E. and Ph.D. in Aerospace Engineering from the University of Michigan in 1990 and 1993. Mike led the DAKOTA project, a “… toolkit that provides a flexible, extensible interface between analysis codes and iterative systems analysis methods…”, for 15 years (1994-2009) and now leads algorithm research and development activities related to DAKOTA. Mike’s research interests include uncertainty quantification, design under uncertainty, surrogate-based optimization, and high-performance computing, with application to stockpile stewardship and energy initiatives through the NNSA ASC, DOE ASCR, and DOE SciDAC programs.
Mike is an Associate Fellow of the American Institute of Aeronautics and Astronautics (AIAA) and a member of the Society for Industrial and Applied Mathematics (SIAM), the International Society for Structural and Multidisciplinary Optimization (ISSMO), and the United States Association for Computational Mechanics (USACM). He currently serves as a member of the AIAA Nondeterministic Approaches Technical Committee and on the editorial board for the International Journal for Uncertainty Quantification. A number of his publications are available on the DAKOTA web site.
In the simulation of complex physics, multiple model forms of varying fidelity and resolution are commonly available. In computational fluid dynamics, for example, common model fidelities include potential flow, inviscid Euler, Reynolds-averaged Navier-Stokes, and large eddy simulation, which may be further augmented by subgrid-scale model selections and spatio-temporal discretization levels. In this presentation, we focus on novel algorithms that simultaneously exploit multiple model forms and multiple resolutions, both for uncertainty quantification (UQ) and for optimization under uncertainty (OUU). These hybrid methods exploit multifidelity methods across the model form hierarchy in combination with multilevel accelerators across an associated discretization hierarchy, manifesting as multilevel control variate Monte Carlo and multilevel polynomial expansion methods in the UQ case and recursive trust-region and multigrid optimization in the OUU case. These techniques will be demonstrated for both model problems and engineered systems, and will be placed within the broader context of algorithm research and development within the Dakota project at Sandia.
Dr. Eldred is being hosted by Prof. Duraisamy (Aerospace Engineering)