MechE Colloquium: Lost in Space: Design Manifolds Can Accelerate Design and Optimization Iterations Several Fold
Event details
| Date | 12.11.2024 |
| Hour | 12:00 › 13:00 |
| Speaker | Prof. Mark Fuge, Artificial Intelligence in Engineering Design, ETHZ |
| Location | Online |
| Category | Conferences - Seminars |
| Event Language | English |
Abstract: When designing complex geometry like the surface of a turbine blade, engineers face a choice. They can use many surface control points (equivalently, design variables) to achieve subtle changes that can lead to potentially important performance improvements — and run the risk of themselves (or their optimizers) getting lost in the (often exponentially) larger design space that results. Or they can play it safe. Use a lower-dimensional, standard design representation that they can tractably explore and optimize — and run the risk of settling with lower-performance designs. In this talk, I advocate for a different path; one that seemingly gets the best of both worlds. I propose learning a Design Manifold — a low-dimensional, non-linear subspace via Generative Models — that captures the main ways in which a design space varies, and how we have used it to accelerate gradient-free optimization time by 10x compared to traditional representations and 2-3x compared to State of the Art techniques, among other benefits. Specifically, I'll present recent work that my group completed as part of the ARPA-E DIFFERENTIATE program and NSF's CAREER program, including applications of Inverse Design for Aerodynamic and Heat Transfer surfaces, among other examples, and present a newly developed technique from my group called "Least Volume" which automatically produces manifolds with the smallest needed dimension that preserves a datasets Topological properties. Time permitting, I'll end with recent work we have done to quantify under what data and model training regimes using Data-Driven Design methods is actually worth it, in the sense that it produces sufficient benefit to offset data generation and training costs.
Biography: Mark Fuge is Professor and Chair of Artificial Intelligence in Engineering Design at ETH Zürich. His staff and students study fundamental scientific and mathematical questions behind how humans and computers can work together to design better complex engineered systems, from the molecular scale all the way to systems as large as aircraft and ships using tools from Computer Science (such as machine learning, artificial intelligence, and submodular optimization) and Applied Mathematics. He completed his Ph.D. from UC Berkeley and has received an NSF CAREER Award, a DARPA Young Faculty Award, and a National Defense Science and Engineering Graduate (NDSEG) Fellowship. For his teaching, he has received the 2023 E. Robert Kent Outstanding Teaching Award for Junior Faculty. He gratefully acknowledges prior and current support from NSF, DARPA, ARPA-E, NIH, ONR, and Lockheed Martin, as well as the tireless efforts of his current and former graduate students and postdocs, upon whose coattails he has been graciously riding since 2015.
Biography: Mark Fuge is Professor and Chair of Artificial Intelligence in Engineering Design at ETH Zürich. His staff and students study fundamental scientific and mathematical questions behind how humans and computers can work together to design better complex engineered systems, from the molecular scale all the way to systems as large as aircraft and ships using tools from Computer Science (such as machine learning, artificial intelligence, and submodular optimization) and Applied Mathematics. He completed his Ph.D. from UC Berkeley and has received an NSF CAREER Award, a DARPA Young Faculty Award, and a National Defense Science and Engineering Graduate (NDSEG) Fellowship. For his teaching, he has received the 2023 E. Robert Kent Outstanding Teaching Award for Junior Faculty. He gratefully acknowledges prior and current support from NSF, DARPA, ARPA-E, NIH, ONR, and Lockheed Martin, as well as the tireless efforts of his current and former graduate students and postdocs, upon whose coattails he has been graciously riding since 2015.
Practical information
- General public
- Free