Open AccessDATA-CONSISTENT SOLUTIONS TO STOCHASTIC INVERSE PROBLEMS USING A PROBABILISTIC MULTI-FIDELITY METHOD BASED ON CONDITIONAL DENSITIES
Author(s) -
Lukas Bruder,
Michael W. Gee,
Timothy Wildey
Publication year - 2020
Publication title -
international journal for uncertainty quantification
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.664
H-Index - 21
eISSN - 2152-5099
pISSN - 2152-5080
DOI - 10.1615/int.j.uncertaintyquantification.2020030092
Subject(s) - computer science , uncertainty quantification , monte carlo method , fidelity , flexibility (engineering) , probabilistic logic , gaussian process , algorithm , mathematical optimization , computation , measure (data warehouse) , surrogate model , bayesian probability , gaussian , machine learning , data mining , mathematics , artificial intelligence , statistics , telecommunications , physics , quantum mechanics
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