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Journal Articles

An Automated Machine Learning-Genetic Algorithm Framework With Active Learning for Design Optimization

Opeoluwa Owoyele, Pinaki Pal, Alvaro Vidal Torreira

Publisher: ASME

J. Energy Resour. Technol. August 2021, 143(8): 082305.

Paper No: JERT-21-1175

https://doi.org/10.1115/1.4050489

Published Online: April 9, 2021

View Article titled, An Automated Machine Learning-Genetic Algorithm Framework With Active Learning for Design Optimization

Topics: Algorithms, Design, Errors, Machinery, Optimization, Computational fluid dynamics, Simulation, Engines

Journal Articles

A Novel Active Optimization Approach for Rapid and Efficient Design Space Exploration Using Ensemble Machine Learning

Opeoluwa Owoyele, Pinaki Pal

Publisher: ASME

J. Energy Resour. Technol. March 2021, 143(3): 032307.

Paper No: JERT-20-1642

https://doi.org/10.1115/1.4049178

Published Online: December 16, 2020

View Article titled, A Novel Active Optimization Approach for Rapid and Efficient Design Space Exploration Using Ensemble Machine Learning

Topics: Algorithms, Combustion, Design, Machine learning, Optimization

Proceedings Papers

An Automated Machine Learning-Genetic Algorithm (AutoML-GA) Framework With Active Learning for Design Optimization

Opeoluwa Owoyele, Pinaki Pal, Alvaro Vidal Torreira

Proc. ASME. ICEF2020, ASME 2020 Internal Combustion Engine Division Fall Technical Conference, V001T06A014, November 4–6, 2020

Publisher: American Society of Mechanical Engineers

Paper No: ICEF2020-3000

https://doi.org/10.1115/ICEF2020-3000

View Paper titled, An Automated Machine Learning-Genetic Algorithm (AutoML-GA) Framework With Active Learning for Design Optimization

Topics: Algorithms, Design, Machinery, Optimization, Simulation, Computational fluid dynamics, Internal combustion engines, Cylinders, Diesel engines, Errors

Journal Articles

Engine Combustion System Optimization Using Computational Fluid Dynamics and Machine Learning: A Methodological Approach

Jihad A. Badra, Fethi Khaled, Meng Tang, Yuanjiang Pei, Janardhan Kodavasal, Pinaki Pal, Opeoluwa Owoyele, Carsten Fuetterer, Brenner Mattia, Farooq Aamir

Publisher: ASME

J. Energy Resour. Technol. February 2021, 143(2): 022306.

Paper No: JERT-20-1594

https://doi.org/10.1115/1.4047978

Published Online: August 27, 2020

View Article titled, Engine Combustion System Optimization Using Computational Fluid Dynamics and Machine Learning: A Methodological Approach

Topics: Algorithms, Computational fluid dynamics, Design, Engines, Machine learning, Optimization, Pistons, Machinery, Robustness, Internal combustion engines

Proceedings Papers

A Novel Active Optimization Approach for Rapid and Efficient Design Space Exploration Using Ensemble Machine Learning

Opeoluwa Owoyele, Pinaki Pal

Proc. ASME. ICEF2019, ASME 2019 Internal Combustion Engine Division Fall Technical Conference, V001T06A006, October 20–23, 2019

Publisher: American Society of Mechanical Engineers

Paper No: ICEF2019-7237

https://doi.org/10.1115/ICEF2019-7237

View Paper titled, A Novel Active Optimization Approach for Rapid and Efficient Design Space Exploration Using Ensemble Machine Learning

Topics: Design, Machine learning, Optimization, Algorithms, Combustion, Particle swarm optimization, Cylinders, Engines, Flow (Dynamics), Fuels

Proceedings Papers

Engine Combustion System Optimization Using CFD and Machine Learning: A Methodological Approach

Jihad Badra, Fethi Khaled, Meng Tang, Yuanjiang Pei, Janardhan Kodavasal, Pinaki Pal, Opeoluwa Owoyele, Carsten Fuetterer, Mattia Brenner, Aamir Farooq

Proc. ASME. ICEF2019, ASME 2019 Internal Combustion Engine Division Fall Technical Conference, V001T06A007, October 20–23, 2019

Publisher: American Society of Mechanical Engineers

Paper No: ICEF2019-7238

https://doi.org/10.1115/ICEF2019-7238

View Paper titled, Engine Combustion System Optimization Using CFD and Machine Learning: A Methodological Approach

Topics: Combustion systems, Computational fluid dynamics, Engines, Machine learning, Optimization, Pistons

Journal citation

NARROW

An Automated Machine Learning-Genetic Algorithm Framework With Active Learning for Design Optimization

A Novel Active Optimization Approach for Rapid and Efficient Design Space Exploration Using Ensemble Machine Learning

An Automated Machine Learning-Genetic Algorithm (AutoML-GA) Framework With Active Learning for Design Optimization

Engine Combustion System Optimization Using Computational Fluid Dynamics and Machine Learning: A Methodological Approach

A Novel Active Optimization Approach for Rapid and Efficient Design Space Exploration Using Ensemble Machine Learning

Engine Combustion System Optimization Using CFD and Machine Learning: A Methodological Approach

Journals

Conference Proceedings

eBooks

Resources

Opportunities

An Automated Machine Learning-Genetic Algorithm Framework With Active Learning for Design Optimization

A Novel Active Optimization Approach for Rapid and Efficient Design Space Exploration Using Ensemble Machine Learning

An Automated Machine Learning-Genetic Algorithm (AutoML-GA) Framework With Active Learning for Design Optimization

Engine Combustion System Optimization Using Computational Fluid Dynamics and Machine Learning: A Methodological Approach

A Novel Active Optimization Approach for Rapid and Efficient Design Space Exploration Using Ensemble Machine Learning

Engine Combustion System Optimization Using CFD and Machine Learning: A Methodological Approach

Journals

Conference Proceedings

eBooks

Resources

Opportunities

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