Mechanical and Civil Engineering Seminar

Title: "Data-Driven Modeling of Turbulent Flows: Nonlinear Modal Dynamics and Transient Forecasting"

Abstract: Turbulent flows pose two closely related challenges for data-driven modeling: predicting high-dimensional transient evolution and extracting the nonlinear interactions that mediate energy transfer across scales. This talk presents two recent methods that address these problems from complementary perspectives. First, I will present triadic orthogonal decomposition, a new framework for revealing nonlinear flow physics through coherent structures that optimally capture spectral momentum transfer. TOD identifies coupled modal interactions, quantifies their energy exchange, and localizes the regions where nonlinear transfer occurs. Second, I will introduce space–time projection, a forecasting framework based on extended space–time POD modes that provides an interpretable and competitive approach for predicting time-resolved flow data. The method naturally combines dimensionality reduction and time-delay embedding, requires minimal tuning, and has been shown to perform strongly on both transient and statistically stationary high-dimensional datasets. Applications to canonical and engineering turbulent flows, using both numerical and experimental data, illustrate how these methods address two key aspects in the data-driven forecasting and analysis of turbulent flows.

Bio: Oliver Schmidt is an Associate Professor in the Department of Mechanical and Aerospace Engineering at the University of California San Diego. He earned his Ph.D. in Aeronautical Engineering from the University of Stuttgart in 2014 and subsequently held a postdoctoral position in Mechanical and Civil Engineering at the California Institute of Technology before joining UC San Diego. Schmidt's research focuses on the simulation and data-driven modeling of complex turbulent flows, with an emphasis on both method development and real-world applications. His group develops advanced tools for reduced-complexity modeling, including modal decomposition techniques, mesh-free numerical methods, and stochastic modeling approaches. These methods are applied across a range of engineering and natural systems, including aeroacoustics, aero-optics, noise control, thermal management, and design optimization. He is best known for his contributions to modal decomposition of turbulent flows, widely disseminated through review articles and open-source software that has been downloaded thousands of times and adopted by researchers worldwide. His work is currently supported by the NSF, ONR, AFOSR, and DOE. Schmidt is a recipient of the NSF CAREER award and was recently named one of ASME's Rising Stars of Mechanical Engineering. He currently serves as co-chair of the AIAA Reduced-Complexity Modeling Discussion Group, leading a community challenge for data-driven model reduction in turbulent flows that provides benchmark datasets with baselines from both machine learning and classical model-order reduction methods.