Modern aircraft represent some of the most complex, performance-driven engineering systems ever conceived and built. Surprisingly, many high-level relationships and constraints on aircraft performance can be encoded via the feasible set of a geometric program. This observation gives us a reliable and efficient way to solve conceptual design problems. Using optimal dual variables, we can also quantify performance sensitivities, better understand tradeoffs, and guide higher fidelity analysis and optimization. We will discuss modeling techniques that have been successful in transforming aircraft design problems to GPs, ongoing research in GP modeling, fitting of GP-compatible models from data, and possible implications for design of large multidisciplinary systems.
Warren is an assistant professor of Aeronautics and Astronautics as MIT. He completed his Ph.D. in Electrical Engineering and Computer Science at Berkeley in 2013 under the supervision of Pieter Abbeel. He received his BS in Aerospace Engineering from MIT. He spent 2013-2014 at Boeing Commercial Airplanes Product Development working on optimization problems in composite manufacturing. His current research interests include convex optimization, operations research, and decision-making in aerospace and manufacturing systems.