Louisiana State University

"Machine Learning Techniques for Simulation-Driven Design Optimization" by Opeoluwa (Ope) Owoyele


An important component of the design process of new reactive flow devices lies in optimizing them for efficiency under constraints relating to undesirable emissions, thermo-mechanical limits, and stability. Computational modeling can play a vital role in this process, whereby design optimization can be performed using computational fluid dynamics (CFD) simulations to identify promising designs for experimental prototyping. However, CFD simulations of such systems are compute-intensive because they involve capturing multi-physical processes that include turbulent gas dynamics, liquid spray injection and breakup, chemical kinetics, heat transfer, and their complex interactions. In this talk, I will present a mixture of deep experts approach that automatically divides modeling tasks amongst specialized learners, leading to simulations that capture experimental trends with reduced computational costs. Efficient simulation-driven design optimization depends, not solely on tractable and predictive computational models, but also on optimizers that can drive design decisions by utilizing these simulations to quickly identify promising designs. Accordingly, I will also talk about a novel approach that employs reinforcement learning to rapidly discover domain-specific and simulation-efficient optimizers. I will conclude my talk by outlining lingering challenges and future directions.

Dial-In Information

TBD- visit link below for updates.

Wednesday, December 7, 2022 at 3:00pm to 4:00pm

Louisiana Digital Media Center, Theatre

Event Type

Lectures & Presentations

Target Audience

Students, Faculty, Staff





Center for Computation & Technology
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