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Volume 14, 2024 / Issue 4

DOI: 10.1615/Int.J.UncertaintyQuantification.v14.i4

Table of Contents:

MODEL ERROR ESTIMATION USING PEARSON SYSTEM WITH APPLICATION TO NONLINEAR WAVES IN COMPRESSIBLE FLOWS Ferdinand Uilhoorn

Ferdinand Uilhoorn
Warsaw University of Technology

1-19 pages
DOI: 10.1615/Int.J.UncertaintyQuantification.2023048277

DECISION THEORETIC BOOTSTRAPPING Peyman Tavallali, Hamed Hamze Bajgiran, Danial J. Esaid, Houman Owhadi

Peyman Tavallali
Data Scientist

Hamed Hamze Bajgiran
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California 91109, USA

Danial J. Esaid
NASA Jet Propulsion Laboratory

Houman Owhadi
Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA

21-38 pages
DOI: 10.1615/Int.J.UncertaintyQuantification.2023038552

UNCERTAINTY QUANTIFICATION AND GLOBAL SENSITIVITY ANALYSIS OF SEISMIC FRAGILITY CURVES USING KRIGING Clement Gauchy, C. Feau, Josselin Garnier

Clement Gauchy
CEA Saclay

C. Feau
Université Paris-Saclay, CEA, Service d'Études Mécaniques et Thermiques, 91191, Gif-sur-Yvette, France

Josselin Garnier
Centre de Mathématiques Appliquées, Ecole Polytechnique, Institut Polytechnique de Paris, 91128 Palaiseau Cedex, France

39-63 pages
DOI: 10.1615/Int.J.UncertaintyQuantification.2023046480

STOCHASTIC GALERKIN METHOD AND PORT-HAMILTONIAN FORM FOR LINEAR FIRST-ORDER ORDINARY DIFFERENTIAL EQUATIONS Roland Pulch, Olivier Sète

Roland Pulch
Institute for Mathematics and Computer Science, University of Greifswald, Walther-Rathenau-Str. 47, D-17489 Greifswald, Germany

Olivier Sète
Universität Greifswald

65-82 pages
DOI: 10.1615/Int.J.UncertaintyQuantification.2024050099

EXTREME LEARNING MACHINES FOR VARIANCE-BASED GLOBAL SENSITIVITY ANALYSIS John E. Darges, Alen Alexanderian, Pierre A. Gremaud

John E. Darges
North Carolina State University

Alen Alexanderian
North Carolina State University

Pierre A. Gremaud
Department of Mathematics, North Carolina State University, Raleigh, NC; The Graduate School, North Carolina State University, Raleigh, NC

83-103 pages
DOI: 10.1615/Int.J.UncertaintyQuantification.2024049519

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