Data to Decisions: Computational Methods for the Next Generation of Engineering Systems
Tuesday, October 17, 2017
1:30PM – 2:30PM
New technologies are changing the way we think about designing and operating engineering systems. For example, in next-generation aircraft the combination of sensing technologies and computational power brings new opportunities for data-driven modeling and data-driven decision-making. Yet data alone cannot deliver the levels of predictive confidence and modeling reliability demanded for these systems. For that, we must build on the decades of progress in rigorous physics-based modeling and associated uncertainty quantification. This talk discusses our work at the intersection of physics-based and data-driven modeling, with a focus on the design of next-generation aircraft. We show how adaptive reduced models combined with data-driven learning enable dynamic decision-making onboard a structural-condition-aware UAV. We show how multifidelity formulations exploit a rich set of information sources to achieve multidisciplinary design under uncertainty for future aircraft concepts.
Karen E. Willcox is Professor of Aeronautics and Astronautics at the Massachusetts Institute of Technology. She is also Co-Director of the MIT Center for Computational Engineering and formerly the Associate Head of the MIT Department of Aeronautics and Astronautics. She has served on the faculty at MIT for 16 years. Prior to that, she worked at Boeing Phantom Works with the Blended-Wing-Body aircraft design group. Her research at MIT 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 DiaMonD Multifaceted Mathematics Capability Center on Mathematics at the Interfaces of Data, Models, and Decisions. 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 80 papers in peer-reviewed journals and advised more than 40 graduate students, including 16 PhD students.
Hosted by J. Tinsley Oden