Welcome to
The Data-driven Modeling and Optimization Lab
@ LSU
Computational Modeling, Machine Learning, Design Optimization
what we do ...
The DMO Lab @ LSU performs research at the intersection of numerical simulations, data science, machine learning, and design optimization on high-performance computing platforms. Applications of interest are the development of reduced-order modeling of energy devices, automated discovery of design optimizers, and biomass processing.
Research Interests
Multiphysics simulations
Turbulent flows, heat transfer, reacting flows, multiphase flow simulations, etc. The development of reduced-order models that retain reasonable accuracy while ensuring computational tractability are of particular interest.
Scientific machine learning and data-driven Modeling
Using experimental and high-fidelity data to build machine learning models. Specific interests include encoding physics in machine learning models, uncertainty quantification, and inverse design.
Engineering design optimization
Developing novel design optimization algorithms for expensive black-box functions (e.g., CFD simulations). The goal is to discover new optimizers that achieve desired design objectives using very few function calls.
“What we know is a drop, what we don't know is an ocean.”
Isaac Newton
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