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DOE OSTI · 2999140

Assessing and Enabling Trustworthy Predictions for High-Consequence Decisions

Portone, Teresa [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)] (ORCID:0000000263800852)·White, Rebekah Dale [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)] (ORCID:0009000283668948)·Bandy, Rileigh Jade [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)] (ORCID:000000025311068X)
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Abstract

Predictions from physics-based computational models provide critical information to inform high consequence decisions, e.g., engineering design decisions. The ability to assess the reliability of such predictions is therefore critical. However, to date, reliability assessment rely heavily on expert judgment and qualitative arguments. This report details the efforts of LDRD 233072 to develop quantitative methods to assess reliability of model predictions, especially in the context of simplifying assumptions that can impact their reliability.

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Portone, Teresa [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)] (ORCID:0000000263800852), White, Rebekah Dale [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)] (ORCID:0009000283668948), Bandy, Rileigh Jade [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)] (ORCID:000000025311068X). 2026-09-01. Assessing and Enabling Trustworthy Predictions for High-Consequence Decisions. https://doi.org/10.2172/2999140

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