Publications

Peer-reviewed Journal Articles

  1. Ikponmwoba, E., Ukorigho, O., Moitra, P., Pan, D., Gartia, M. R., & Owoyele, O. (2022). A Machine Learning Framework for Detecting COVID-19 Infection Using Surface-Enhanced Raman Scattering. Biosensors.
  2. Owoyele, O. & Pal, P. (2022). ChemNODE: A neural ordinary differential equations approach for chemical kinetics solvers. Energy and AI.
  3. Owoyele, O., Pal, P., Torreira, AV., Wilde, M., Probst, P. & Senecal, KP (2021). Accelerating CFD-driven design optimization via an automated machine learning-genetic algorithm approach. Frontiers in Mechanical Engineering.
  4. Owoyele, O. & Pal, P. (2021). Application of a novel machine learning-based optimization algorithm (ActivO) for accelerating simulation-driven engine design. Applied Energy.
  5. Owoyele, O., Pal, P. & Torreira, AV (2021). An automated machine learning-genetic algorithm framework with active learning for design optimization. Journal of energy resources technology.
  6. Owoyele, O., Kundu, P. & Pal, P (2020). Efficient bifurcation and tabulation of multi-dimensional combustion manifolds using deep mixture of experts: an a priori Proceedings of the Combustion Institute.
  7. Owoyele, O. & Pal, P. (2020). A novel active optimization approach for rapid and efficient design space exploration using ensemble machine learning. Journal of energy resources technology.
  8. Badra, J., Khaled, F., Tang, M., Pei, Y., Kodavasal, J., Pal, P., Owoyele, O., Futterer, C, Brenner, M. & Farooq, A. (2020). Engine Combustion System Optimization using CFD and Machine Learning: A Methodological Approach. Journal of energy resources technology.
  9. Owoyele, O., Kundu, P., Ameen, M. M., Echekki, T., & Som, S. (2019). Application of deep artificial neural networks to multi-dimensional flamelet libraries and spray flames. International Journal of Engine Research.
  10. Owoyele, O. & Echekki, T. (2017). Toward computationally efficient combustion DNS with complex fuels via principal component transport. Combustion Theory and Modelling.
  11. Owoyele, O., Ferguson, S., & O’Connor, B. T. (2015). Performance analysis of a thermoelectric cooler with a corrugated architecture. Applied Energy.

Book Chapters

  1. Badra, J., Owoyele, O., Pal, P., & Som, S. (2022). A machine learning-genetic algorithm approach for rapid optimization of internal combustion engines. In Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines(pp. 125-158). Elsevier.
  2. Owoyele, O., & Pal, P. (2022). Machine learning–driven sequential optimization using dynamic exploration and exploitation. In Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines(pp. 159-181). Elsevier.

Conference Presentations & Proceedings

  1. Owoyele, O., Pal, P., 2022. A Mixture of Experts Approach for Efficient Representation of Combustion Manifolds. Society of Industrial and Applied Mathematics TX-LA Annual Meeting.
  2. Kumar, T., Pal, P., Nunno, A., Wu, S., Owoyele O., Joly, M., Tretiak, D., 2022. Development of a Data-Driven Wall Model for Large-Eddy Simulation of Gas Turbine Film Cooling Flows. 75th Annual Meeting of the APS Division of Fluid Dynamics.
  3. Owoyele, O., Pal, P., Torreira, AV. An Automated machine learning-genetic algorithm (AutoML-GA) framework with Active Learning for Design Optimization. ASME ICEF 2020.
  4. Owoyele, O., Pal, P., 2020. Predicting the evolution of chemical species using Neural ODEs. 73rd Annual Meeting of the APS Division of Fluid Dynamics.
  5. Owoyele, O., Pal, P., 2020. Application of Machine Learning  for  Turbulent  Combustion Modeling and Engine Design Optimization. AEC Program Review Meeting. August 13th, 2020.
  6. Owoyele, O., Pal, P., Som, S., 2020. Machine Learning and HPC for Accelerating the Engine Design Process. 2020 European CONVERGE User Conference.
  7. Owoyele, O., Pal, P., Kundu, P., 2020. Application of Machine Learning for Turbulent Combustion Modeling and Engine Design Optimization. AEC Program Review Meeting. February 6th, 2020.
  8. Owoyele, O., Kundu, P., Pal, P., 2019. A novel deep learning framework for efficient parameterization of high-dimensional flamelet manifolds. 72nd Annual Meeting of the APS Division of Fluid Dynamics.
  9. Owoyele, O., Pal., P, 2019. ActivO: A Novel Active Optimization Approach Using Ensemble Machine Learning. 2019 Argonne postdoctoral symposium (Poster).
  10. Pal, P, Owoyele, O., Chao, Xu, Som, S., 2019. Overview of RDE Combustion Modeling and ML HPC Based Design Optimization Capabilities at Argonne. VERIFI workshop on multi-phase and reacting flows for aero-propulsion.
  11. Kundu, P., Paoli, R., Demir A., Owoyele, O., Drozda, T., Baurle, R., 2019. Model development for scramjet applications: Vulcan. VERIFI workshop on multi-phase and reacting flows for aero-propulsion.
  12. Owoyele, O., Pal., P, 2019. A novel active optimization approach for rapid and efficient design space exploration using ensemble machine learning. VERIFI workshop on multi-phase and reacting flows for aero-propulsion (Poster).
  13. Owoyele, O., Pal., P, 2019. A novel active optimization approach for rapid and efficient design space exploration using ensemble machine learning. VERIFI workshop on Ignition Modeling (poster).
  14. Owoyele, O., Kundu, P., & Pal, P., 2019. A novel deep learning framework for efficient parameterization of high-dimensional flamelet manifolds. Bulletin of the American Physical Society.
  15. Owoyele, O., Pal, P., Som, S., 2019. Accelerating Design Optimization Using Machine Learning and HPC. 2019 CONVERGE User Conference, New Orleans, LA.
  16. Owoyele, O., Pal, P., Som, S., 2019. Accelerating Engine Design Optimization Using CFD,
  17. Machine Learning and High-performance Computing. AEC review meeting, Southfield, MI.
  18. Owoyele, O., VanEssendelft, D., Buchheit, K., Jordan, T., 2018. Accelerating computational fluid dynamics using Tensorflow. 2018 Workshop on Multiphase Flow Science, Houston, TX. (Poster).
  19. Owoyele, O., Echekki, T., 2016. Direct Numerical Simulation of Combustion Using Principal Component Analysis. 69th Annual Meeting of the APS Division of Fluid Dynamics.