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Journal Articles

Sequential Design Process for Screening and Optimization of Robustness and Reliability Based on Finite Element Analysis and Meta-Modeling

Tim Brix Nerenst, Martin Ebro, Morten Nielsen, Kanishk Bhadani, Gauti Asbjörnsson, Tobias Eifler, Kim Lau Nielsen
Journal: Journal of Computing and Information Science in Engineering
Publisher: ASME
Article Type: Research Papers
J. Comput. Inf. Sci. Eng. August 2022, 22(4): 040902.
Paper No: JCISE-21-1325
https://doi.org/10.1115/1.4053074
Published Online: February 7, 2022
View Article titled, Sequential Design Process for Screening and Optimization of Robustness and Reliability Based on Finite Element Analysis and Meta-Modeling
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Topics: Design, Finite element analysis, Optimization, Robustness, Simulation, Reliability, Experimental design, Metamodeling, Failure
Journal Articles

Physics-Based Gaussian Process Method for Predicting Average Product Lifetime in Design Stage

Xinpeng Wei, Daoru Han, Xiaoping Du
Journal: Journal of Computing and Information Science in Engineering
Publisher: ASME
Article Type: Research Papers
J. Comput. Inf. Sci. Eng. August 2021, 21(4): 041006.
Paper No: JCISE-20-1217
https://doi.org/10.1115/1.4049509
Published Online: February 11, 2021
View Article titled, Physics-Based Gaussian Process Method for Predicting Average Product Lifetime in Design Stage
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Topics: Design, Failure, Physics, Reliability, Stochastic processes, Event history analysis, Statistics, Testing, Errors, Experimental design
Journal Articles

A Statistical Design of Experiments Approach to Machine Learning Model Selection in Engineering Applications

T. Munger, S. Desa
Journal: Journal of Computing and Information Science in Engineering
Publisher: ASME
Article Type: Research Papers
J. Comput. Inf. Sci. Eng. February 2021, 21(1): 011008.
Paper No: JCISE-20-1049
https://doi.org/10.1115/1.4047915
Published Online: August 17, 2020
View Article titled, A Statistical Design of Experiments Approach to Machine Learning Model Selection in Engineering Applications
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Topics: Experimental design, Machine learning, Taguchi methods, Algorithms, Engineering systems and industry applications