AI Engineering Seminar: Beverley McKeon, Stanford
Part of 2025-26 AI Series
Content
Information from Data: Bridging Experimental and ML Capabilities
Thursday, February 12, 2026
11 AM - 12 Noon
Kellogg Conference Room, ELab-II
Presented by:
Beverley J. McKeon
Mechanical Engineering
Stanford University
Abstract:
The availability of data characterizing aspects of complex flow problems in fluid mechanics has advanced dramatically over the course of the last decade. Concurrent advances in ML/AI have the potential to revolutionize the engineering of fluid systems and create new technological capabilities. While the range of practical success stories grows almost daily, some gaps remain between conceptual development and proof-of-concept studies, and practical engineering implementation for fluid systems, in which the (spatio-temporal) dynamics and sparse observations may dictate the appropriate algorithms and approaches (and, of course, for which we know the Navier-Stokes equations). In this talk, I will focus on flows that either cannot yet be computed or observed in full, applying appropriately selected and/or developed algorithms to imperfect experimental observations.
One more AI Engineering Seminars is scheduled for Thursday March 11, presented by Faez Ahmed, MIT