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Leveraging explainable AI to characterize floating-point exceptions in linear solvers.

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Title: Leveraging explainable AI to characterize floating-point exceptions in linear solvers.
Authors: Laguna, Ignacio1 (AUTHOR) ilaguna@llnl.gov
Source: Journal of Systems & Software. Apr2026, Vol. 234, pN.PAG-N.PAG. 1p.
Database: Business Source Ultimate
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DbLabel: Business Source Ultimate
An: 190696972
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Leveraging explainable AI to characterize floating-point exceptions in linear solvers.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Systems+%26+Software%22">Journal of Systems & Software</searchLink>. Apr2026, Vol. 234, pN.PAG-N.PAG. 1p.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=bsu&AN=190696972
RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1016/j.jss.2025.112757
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      – Code: eng
        Text: English
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        StartPage: N.PAG
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      – TitleFull: Leveraging explainable AI to characterize floating-point exceptions in linear solvers.
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              M: 04
              Text: Apr2026
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              Y: 2026
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              Value: 234
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