Irradiation increases the yield stress and embrittles light water reactor(LWR)pressure vessel steels.In this study,we demonstrate some of the potential benefits and risks of using machine learning models to predict ir...Irradiation increases the yield stress and embrittles light water reactor(LWR)pressure vessel steels.In this study,we demonstrate some of the potential benefits and risks of using machine learning models to predict irradiation hardening extrapolated to low flux,high fluence,extended life conditions.The machine learning training data included the Irradiation Variable for lower flux irradiations up to an intermediate fluence,plus the Belgian Reactor 2 and Advanced Test Reactor 1 for very high flux irradiations,up to very high fluence.Notably,the machine learning model predictions for the high fluence,intermediate flux Advanced Test Reactor 2 irradiations are superior to extrapolations of existing hardening models.The successful extrapolations showed that machine learning models are capable of capturing key intermediate flux effects at high fluence.Similar approaches,applied to expanded databases,could be used to predict hardening in LWRs under life-extension conditions.展开更多
Obtaining accurate estimates of machine learning model uncertainties on newly predicted data is essential for understanding the accuracy of the model and whether its predictions can be trusted.A common approach to suc...Obtaining accurate estimates of machine learning model uncertainties on newly predicted data is essential for understanding the accuracy of the model and whether its predictions can be trusted.A common approach to such uncertainty quantification is to estimate the variance from an ensemble of models,which are often generated by the generally applicable bootstrap method.In this work,we demonstrate that the direct bootstrap ensemble standard deviation is not an accurate estimate of uncertainty but that it can be simply calibrated to dramatically improve its accuracy.We demonstrate the effectiveness of this calibration method for both synthetic data and numerous physical datasets from the field of Materials Science and Engineering.The approach is motivated by applications in physical and biological science but is quite general and should be applicable for uncertainty quantification in a wide range of machine learning regression models.展开更多
基金D.M.,H.W.,R.J.,and T.M.gratefully acknowledge partial funding from NSF SI2-SSI award 1148011the Light Water Reactor Sustainability program,and Nuclear Energy University Program (NEUP) 21-24382+1 种基金Y.-c.L.gratefully acknowledge the financial support from Graduate Student Study Abroad Program (GSSAP) (107-2917-I-006-008),project (110-2222-E-006-008) from the Ministry of Science and Technology (MOST)the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) and MOST (110-2634-F-006-017) in Taiwan,China.
文摘Irradiation increases the yield stress and embrittles light water reactor(LWR)pressure vessel steels.In this study,we demonstrate some of the potential benefits and risks of using machine learning models to predict irradiation hardening extrapolated to low flux,high fluence,extended life conditions.The machine learning training data included the Irradiation Variable for lower flux irradiations up to an intermediate fluence,plus the Belgian Reactor 2 and Advanced Test Reactor 1 for very high flux irradiations,up to very high fluence.Notably,the machine learning model predictions for the high fluence,intermediate flux Advanced Test Reactor 2 irradiations are superior to extrapolations of existing hardening models.The successful extrapolations showed that machine learning models are capable of capturing key intermediate flux effects at high fluence.Similar approaches,applied to expanded databases,could be used to predict hardening in LWRs under life-extension conditions.
基金The National Science Foundation provided financial support for G.P.(Award#1545481)S.D.,A.P.,J.P.E.,and X.Y.(Award#1636950 and 1636910)+1 种基金R.J.and D.M.(Award#1931298)Financial support for A.G.and G.G.was provided by the University of Wisconsin Harvey D.Spangler Professorship.
文摘Obtaining accurate estimates of machine learning model uncertainties on newly predicted data is essential for understanding the accuracy of the model and whether its predictions can be trusted.A common approach to such uncertainty quantification is to estimate the variance from an ensemble of models,which are often generated by the generally applicable bootstrap method.In this work,we demonstrate that the direct bootstrap ensemble standard deviation is not an accurate estimate of uncertainty but that it can be simply calibrated to dramatically improve its accuracy.We demonstrate the effectiveness of this calibration method for both synthetic data and numerous physical datasets from the field of Materials Science and Engineering.The approach is motivated by applications in physical and biological science but is quite general and should be applicable for uncertainty quantification in a wide range of machine learning regression models.
