activity
20192026
most citedThe Stabilized Explicit Variable-Load Solver with Machine Learning Acceleration for the Rapid Solution of Stiff Chemical Kinetics

1 citations · 1 across the 6 of their papers we have counts for

collaborators

7 papers

cs.LG2026

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…

physics.chem-ph2025

Quantized Skeletal Learning (QSL): A Differentiable Programming Approach for Skeletal Reduction of Chemical Mechanisms

Opeoluwa Owoyele

This paper presents a data-driven approach, referred to as Quantized Skeletal Learning (QSL), for generating skeletal mechanisms. The approach has two key components: (1) a weight…

cs.LG2021

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…

cs.CE2021

ChemNODE: A Neural Ordinary Differential Equations Approach for Chemical Kinetics Solvers

Opeoluwa Owoyele, Pinaki Pal

Solving for detailed chemical kinetics remains one of the major bottlenecks for computational fluid dynamics simulations of reacting flows using a finite-rate-chemistry approach. T…

cs.LG2021

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…

physics.flu-dyn2019

Efficient bifurcation and parameterization of multi-dimensional combustion manifolds using deep mixture of experts: an a priori study

Opeoluwa Owoyele, Prithwish Kundu, Pinaki Pal

This work describes and validates an approach for autonomously bifurcating turbulent combustion manifolds to divide regression tasks amongst specialized artificial neural networks…