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Journal Articles

Uncertainty Quantification of a Machine Learning Model for Identification of Isolated Nonlinearities With Conformal Prediction

David A. Najera-Flores, Justin Jacobs, D. Dane Quinn, Anthony Garland, Michael D. Todd
Journal: Journal of Verification, Validation and Uncertainty Quantification
Publisher: ASME
Article Type: Research-Article
J. Verif. Valid. Uncert. June 2024, 9(2): 021005.
Paper No: VVUQ-23-1054
https://doi.org/10.1115/1.4064777
Published Online: June 21, 2024
View Article titled, Uncertainty Quantification of a Machine Learning Model for Identification of Isolated Nonlinearities With Conformal Prediction
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Topics: Artificial neural networks, Damage, Machine learning, Noise (Sound), Uncertainty quantification
Journal Articles

Credibility Assessment of Machine Learning in a Manufacturing Process Application

Gregory A. Banyay, Clarence L. Worrell, Scott E. Sidener, Joshua S. Kaizer
Journal: Journal of Verification, Validation and Uncertainty Quantification
Publisher: ASME
Article Type: Research-Article
J. Verif. Valid. Uncert. September 2021, 6(3): 031007.
Paper No: VVUQ-21-1007
https://doi.org/10.1115/1.4051717
Published Online: July 27, 2021
View Article titled, Credibility Assessment of Machine Learning in a Manufacturing Process Application
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Topics: Machine learning, Manufacturing
Journal Articles

Integrating Bayesian Calibration, Bias Correction, and Machine Learning for the 2014 Sandia Verification and Validation Challenge Problem

Wei Li, Shishi Chen, Zhen Jiang, Daniel W. Apley, Zhenzhou Lu, Wei Chen
Journal: Journal of Verification, Validation and Uncertainty Quantification
Publisher: ASME
Article Type: Research-Article
J. Verif. Valid. Uncert. March 2016, 1(1): 011004.
Paper No: VVUQ-15-1007
https://doi.org/10.1115/1.4031983
Published Online: February 19, 2016
View Article titled, Integrating Bayesian Calibration, Bias Correction, and Machine Learning for the 2014 Sandia Verification and Validation Challenge Problem
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Topics: Calibration, Machine learning, Uncertainty, Displacement