A Framework for Inverse Prediction Using Functional Response Data

Ries, Daniel; Zhang, Adah; Derek Tucker, J.; Shuler, Kurtis; Ausdemore, Madeline

doi:10.1115/1.4053752

A Framework for Inverse Prediction Using Functional Response Data

Journal Article · Fri May 20 00:00:00 EDT 2022 · Journal of Computing and Information Science in Engineering

DOI:https://doi.org/10.1115/1.4053752· OSTI ID:1882881

Ries, Daniel ^[1]; Zhang, Adah ^[1]; Derek Tucker, J. ^[1]; Shuler, Kurtis ^[1]; Ausdemore, Madeline ^[2]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Inverse prediction models have commonly been developed to handle scalar data from physical experiments. However, it is not uncommon for data to be collected in functional form. When data are collected in functional form, it must be aggregated to fit the form of traditional methods, which often results in a loss of information. For expensive experiments, this loss of information can be costly. In this study, we introduce the functional inverse prediction (FIP) framework, a general approach which uses the full information in functional response data to provide inverse predictions with probabilistic prediction uncertainties obtained with the bootstrap. The FIP framework is a general methodology that can be modified by practitioners to accommodate many different applications and types of data. We demonstrate the framework, highlighting points of flexibility, with a simulation example and applications to weather data and to nuclear forensics. Results show how functional models can improve the accuracy and precision of predictions.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); Los Alamos National Laboratory (LANL)

Grant/Contract Number:: NA0003525

OSTI ID:: 1882881

Report Number(s):: SAND2022-0974J; 703108

Journal Information:: Journal of Computing and Information Science in Engineering, Journal Name: Journal of Computing and Information Science in Engineering Journal Issue: 1 Vol. 23; ISSN 1530-9827

Publisher:: ASMECopyright Statement

Country of Publication:: United States

Language:: English

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A Framework for Inverse Prediction Using Functional Response Data

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