3 citations · 4 across the 11 of their papers we have counts for
5 papers · 1 filter
A Reinforcement-Learning-Augmented Liquid-Fueled Reactor Network Model for Predicting Lean Blowout in Gas Turbine Combustors
Philip John, Eloghosa Ikponmwoba, Pinaki Pal +1
This study introduces a reinforcement learning (RL) framework for generating optimal liquid-fueled reactors to improve lean blowout (LBO) predictions in gas turbine combustors. Exi…
Autonomous Adaptive Solver Selection for Chemistry Integration via Reinforcement Learning
Eloghosa Ikponmwoba, Opeoluwa Owoyele
The computational cost of stiff chemical kinetics remains a dominant bottleneck in reacting-flow simulation, yet hybrid integration strategies are typically driven by hand-tuned he…
A Competitive Learning Approach for Specialized Models: A Solution for Complex Physical Systems with Distinct Functional Regimes
Okezzi F. Ukorigho, Opeoluwa Owoyele
Complex systems in science and engineering sometimes exhibit behavior that changes across different regimes. Traditional global models struggle to capture the full range of this co…
A novel machine learning-based optimization algorithm (ActivO) for accelerating simulation-driven engine design
Opeoluwa Owoyele, Pinaki Pal
A novel design optimization approach (ActivO) that employs an ensemble of machine learning algorithms is presented. The proposed approach is a surrogate-based scheme, where the pre…
Application of an automated machine learning-genetic algorithm (AutoML-GA) coupled with computational fluid dynamics simulations for rapid engine design optimization
Opeoluwa Owoyele, Pinaki Pal, Alvaro Vidal Torreira +4
In recent years, the use of machine learning-based surrogate models for computational fluid dynamics (CFD) simulations has emerged as a promising technique for reducing the computa…