Willcox Research Group
My research interests lie in computational simulation and optimization of engineering systems, with a specific focus on data to decisions in complex systems. I develop methods that combine physics-based models with data-driven learning for applications in optimization, real-time simulation, control, and uncertainty quantification. I develop methods for multi-fidelity uncertainty quantification and multi-fidelity optimization under uncertainty. I create new methods for managing multiple models and data sources for applications in design and uncertainty quantification.
Enabling a self-aware UAV with dynamic data-driven model reduction
In collaboration with Aurora Flight Sciences and Texas A&M University
Funded by AFOSR Dynamic Data-Driven Application Systems (DDDAS)
Managing multiple sources of information in the design of multidisciplinary systems
In collaboration with Arizona State University, Cornell University, University of Michigan, Santa Fe Institute and Texas A&M University
Funded by AFOSR Multidisciplinary Research Program of the University Research Initiative (MURI)
In collaboration with Aurora Flight Sciences and Akselos
Funded by AFOSR Dynamic Data Driven Application Systems, AFOSR Computational Mathematics, and MIT-SUTD International Design Center
AEOLUS: Advances in Experimental Design, Optimization and Learning for Uncertain Complex Systems
In collaboration with Brookhaven National Laboratory, Massachusetts Institute of Technology, Oak Ridge National Laboratory, and Texas A&M University
Funded by U.S. Department of Energy, Office of Science, Mathematical Multifaceted Integrated Capabilities Center (MMICC)
Air Force Center of Excellence on Multi-Fidelity Modeling of Rocket Combustor Dynamics
In collaboration with University of Michigan, Purdue University and New York University (Courant)
Funded by the Air Force Center of Excellence.
Gappy proper orthogonal decomposition
Our research develops gappy POD methods for flow reconstruction, flow sensing, sensor placement and structural assessment.
Multi-fidelity uncertainty quantification
Multifidelity UQ is the use of multiple approximate models and other sources of information to accelerate UQ tasks such as optimization under uncertainty and estimating mean, variance, probability of failure, and global sensitivity indices.
In collaboration with Colorado State University, Florida State University, Los Alamos National Laboratory, Oak Ridge National Laboratory, Stanford University and University of Texas at Austin
Funded by U.S. Department of Energy, Office of Science, Mathematical Multifaceted Integrated Capabilities Center (MMICC)
QUANTUM: Inference, Simulation, and Optimization of Complex Systems Under Uncertainty
In collaboration with University of Auckland, University of Texas at Austin, Rice University and Warwick University
Funded by DARPA Enabling Quantification of Uncertainty in Physical Systems (EQUiPS)
