Karen E. Willcox, PhD, MNZM

Director, Oden Institute for Computational Engineering and Sciences | Professor of Aerospace Engineering and Engineering Mechanics | W. A. “Tex” Moncrief, Jr. Chair in Simulation-Based Engineering and Sciences | Peter O'Donnell, Jr. Centennial Chair in Computing Systems | External Professor, Santa Fe Institute |

Research in Multi-fidelity uncertainty quantification, multi-fidelity optimization under uncertainty, adaptive reduced models, predictive digital twins, scientific machine learning, and data to decisions in aerospace engineering


201 E 24th Street, POB 4.102
University of Texas at Austin
Austin, TX 78712
kwillcox@oden.utexas.edu
(512) 471-3312
Karen E. Willcox in the Oden Institute visualization lab

Biography

Karen E. Willcox is Director of the Oden Institute for Computational Engineering and Sciences and a Professor of Aerospace Engineering and Engineering Mechanics, at the University of Texas at Austin. She holds the W. A. “Tex” Moncrief, Jr. Chair in Simulation-Based Engineering and Sciences and the Peter O'Donnell, Jr. Centennial Chair in Computing Systems. Prior to joining the Oden Institute in 2018, she spent 17 years as a professor at the Massachusetts Institute of Technology, where she served as Professor of Aeronautics and Astronautics, the founding Co-Director of the MIT Center for Computational Engineering, and the Associate Head of the MIT Department of Aeronautics and Astronautics. She is also an External Professor at the Santa Fe Institute.

Willcox holds a Bachelor of Engineering Degree from the University of Auckland, New Zealand, and masters and PhD degrees from MIT. Prior to becoming a professor at MIT, she worked at Boeing Phantom Works with the Blended-Wing-Body aircraft design group. Her research has produced scalable computational methods for design of next-generation engineered systems, with a particular focus on model reduction as a way to learn principled approximations from data and on multi-fidelity formulations to leverage multiple sources of uncertain information. These methods are widely applied in aircraft system design and environmental policy decision-making. Willcox is currently Co-director of the Department of Energy AEOLUS Multifaceted Mathematics Capability Center on Advances in Experimental Design, Optimal Control, and Learning for Uncertain Complex Systems. She leads an Air Force MURI on optimal design of multi-physics systems and an Air Force Data-Driven Dynamic Applications Systems project team that is developing a self-aware UAV. She has co-authored more than 100 papers in peer-reviewed journals and advised more than 50 graduate students, including 19 PhD students.

In addition to her research pursuits, Willcox is active in education innovation. She served as co-Chair of the MIT Online Education Policy Initiative, co-Chair of the 2013-2014 Institute wide Task Force on the Future of MIT Education, and Chair of the MIT OpenCourseWare Faculty Advisory Board. She is a recognized innovator in the U.S. education landscape, where she is a 2015 recipient of the First in the World Department of Education grant. She continues to direct the MIT Mapping Lab, which develops technologies for the future of digital education.

Willcox is Fellow of the Society for Industrial and Applied Mathematics (SIAM), Fellow of the American Institute of Aeronautics and Astronautics (AIAA), and member of the American Society for Engineering Education (ASEE). She has served in multiple leadership positions within AIAA and SIAM, including leadership roles in the SIAM Activity Group on Computational Science and Engineering and in the AIAA Multidisciplinary Design Optimization Technical Committee. She is Section Editor of SIAM Journal on Scientific Computing and Associate Editor of AIAA Journal. She is a current member of the National Academies Board on Mathematical Sciences and Analytics, and has served on five National Academies studies and review panels. In 2017, she was awarded Member of the New Zealand Order of Merit (MNZM).

Resume

Recent News

February 2020

Congratulations to Elizabeth Qian for having her paper Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems accepted for publication in Physica D: Nonlinear Phenomena. The paper lays the theoretical foundations for a new physics-informed machine learning method for systems governed by nonlinear partial differential equations. The Lift & Learn method uses lifting transformations, which introduce auxiliary variables to expose polynomial structure. This polynomial structure is exploited to achieve non-intrusive learning from simulation snapshot data, through the lens of projection (which preserves polynomial structure). Elizabeth's analysis of the method shows that in some settings Lift & Learn models recover the generalization accuracy of intrusive projection-based reduced models.

Learning physics-based reduced-order models for a single-injector combustion process accepted to AIAA Journal. This work is collaborative with Dr. Cheng Huang on our Air Force Center of Excellence on Multi-fidelity Modeling of Rocket Combustion Dynamics and shows the power of our non-intrusive Lift & Learn model reduction method on a challenging combustion example.

Congratulations to Max Opgenoord and Doug Allaire for having their paper Variance-based sensitivity analysis to support simulation-based design under uncertainty recognized as one of the top 10 most accessed articles in Journal of Mechanical Design in 2019.

Congratulations to Renee Swischuk and Boris Kramer for having their paper Learning physics-based reduced-order models for a single-injector combustion process accepted to AIAA Journal. This work is collaborative with Dr. Cheng Huang on our Air Force Center of Excellence on Multi-fidelity Modeling of Rocket Combustion Dynamics and shows the power of our non-intrusive Lift & Learn model reduction method on a challenging combustion example.

January 2020

Congratulations to Anirban Chaudhuri and Boris Kramer for having their paper Information Reuse for Importance Sampling in Reliability-Based Design Optimization accepted to Reliability Engineering and System Safety.

Congratulations to PhD student Michael Kapteyn for winning the Southwest Research Institute Student Paper Prize for his AIAA Scitech paper Toward predictive digital twins via component-based reduced-order models and interpretable machine learning.

Congratulations to Laurence Cook for his paper Design optimization using multiple dominance relations being accepted to International Journal for Numerical Methods in Engineering.

Congratulations to Remi Lam for his paper Multifidelity dimension reduction via active subspaces being accepted to SIAM Journal on Scientific Computing.

Karen, Anirban and Michael will all be at the 2020 AIAA Scitech meeting in Orlando. MIT PhD student Michael Kapteyn will present Toward predictive digital twins via component-based reduced-order models and interpretable machine learning on Monday January 6. Karen will present Learning physics-based reduced-order models for a single-injector combustion process on Wednesday January 8. MIT postdoc Anirban Chaudhuri will present Multifidelity Cross-Entropy Estimation of Conditional Value-at-Risk for Risk-Averse Design Optimization in the Managing Multiple Sources of Information MURI Special Session on Friday January 10.

December 2019

Congratulations to Alex Marques, Max Opgenoord, Remi Lam and Anirban Chaudhuri for their paper A multifidelity method for locating aeroelastic flutter boundaries being accepted to AIAA Journal. Great to see the collaboration among different group members bringing different aspects of multifidelity modeling together to develop a method that can produce accurate estimates of the flutter boundary at a reduced cost by combining information from low- and high-fidelity aeroelastic models.

November 2019

The video of Karen's invited talk on Predictive Data Science at ICIAM 2019 is posted here. The slides are available here.

Karen is giving an invited talk at SC19. Here are her presentation slides on Predictive Data Science, highlighting our work building an unmanned aerial vehicle and its Digital Twin.

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