Two Jacobs School Engineers Elected AIAA Associate Fellows

Kramer is being recognized "for scientific and engineering contributions to the field of reduced-order modeling and uncertainty quantification for aerospace design."

Kramer’s research is in developing computational methods and theory for model reduction of high-dimensional complex systems, control and uncertainty quantification. He has impacted aerospace design through novel mathematical and computational approaches to nonlinear and data-driven model reduction. One of his unique approaches uses nonlinear transformations of the data to learn better predictive models in structured latent spaces, which has proven to promote stability and accuracy of predictions. He also has advanced the field of risk-based design optimization of aerospace systems to focus the design on rare tail events through coherent risk measures rather than risk-neutral expectations. This can prevent costly project delays and overruns. He is currently involved in several Air Force projects, including the Air Force Research Laboratory – UC San Diego Collaborative Center for Design and Research of Interdisciplinary Systems and the Air Force Office of Scientific Research Initiative (MURI) project on Mathematics of Digital Twins.

Schmidt is recognized “for foundational contributions to modal and nonlinear spectral analysis, turbulent-jet aeroacoustics, and data-driven reduced-complexity modeling and forecasting of aerospace flows."

Schmidt’s research combines high-fidelity numerical simulation, physics-based analysis, and modern data-driven methods to uncover coherent structures in turbulent and multiphysics flows and translate those insights into predictive models. A defining feature of his work is the application of these methods to realistic aerospace flows and configurations, including jet-noise control, unsteady aerodynamics and aeroacoustics, transition, dynamic stall, aero-optics, and hypersonics. By bringing empirical and theoretical approaches together, his research not only advances the fundamental understanding of flow physics but also enables the development of low-order models for prediction, optimization, and control. This work has received support from AFOSR, ONR, ARO, NSF, DOE, AFRL, and LLNL.