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Bayesian optimization approach to quantify the effect of input parameter uncertainty on predictions of numerical physics simulations.

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Title: Bayesian optimization approach to quantify the effect of input parameter uncertainty on predictions of numerical physics simulations.
Authors: McCallum, Samuel G.1, Lerpinière, James E.1, Jensen, Kjeld O.2, Friederich, Pascal3,4 pascal.friederich@kit.edu, Walker, Alison B.1 pascal.friederich@kit.edu
Source: APL Machine Learning. Dec2023, Vol. 1 Issue 4, p1-7. 7p.
Database: Academic Search Ultimate
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  Data: <searchLink fieldCode="JN" term="%22APL+Machine+Learning%22">APL Machine Learning</searchLink>. Dec2023, Vol. 1 Issue 4, p1-7. 7p.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asn&AN=179536937
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        Value: 10.1063/5.0151747
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        Text: English
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              Text: Dec2023
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              Y: 2023
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