The research at the lab focuses on the development of computational methods for the analysis, design, and operation of complex systems. We are currently specifically interested in aspects of optimization, uncertainty quantification, and machine learning for simulation-based design. Often, these methods involve the use of multiple sources of information, which we refer to as multifidelity methods, or more generally, multi-information source methods. Current projects involve the development of computational methods for enabling autonomous materials discovery, the development of optimal algorithms for multi-information source management in design of materials and materials systems, and optimal sample-based uncertainty quantification.
Collaborators
Raymundo Arroyave, Professor of Materials Science and Engineering at Texas A&M University
Danny Perez, Technical Staff Member, T-1, Los Alamos National Laboratory
Ankit Srivastava, Associate Professor of Materials Science and Engineering at Texas A&M University
Ibrahim Karaman, Professor and Department Head of Materials Science and Engineering at Texas A&M University
John Jakeman, Principal Member of the Technical Staff, Sandia National Laboratories
Alex Gorodetsky, Assistant Professor of Aerospace Engineering at University of Michigan
Karen Willcox, NAE, Professor, Director, Oden Institute for Computational Engineering and Sciences at University of Texas, Austin
Current Research
Autonomous Materials Discovery
ULTIMATE
MIS-BO for Microstructure Performance
PSP vs PP
Enabling Self Aware UAVs
Adaptive Multifidelity Experimental Design
Active Subspace Multifidelity Bayesian Optimization
