Search Results - "International journal for uncertainty quantification" :: K.UTB vyhledávací portál

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POLYNOMIAL-CHAOS-BASED KRIGING

Published in International journal for uncertainty quantification

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A COMPREHENSIVE COMPARISON OF TOTAL-ORDER ESTIMATORS FOR GLOBAL SENSITIVITY ANALYSIS

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RECURSIVE CO-KRIGING MODEL FOR DESIGN OF COMPUTER EXPERIMENTS WITH MULTIPLE LEVELS OF FIDELITY

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AUTOMATIC SELECTION OF BASIS-ADAPTIVE SPARSE POLYNOMIAL CHAOS EXPANSIONS FOR ENGINEERING APPLICATIONS

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DEEP LEARNING OF PARAMETERIZED EQUATIONS WITH APPLICATIONS TO UNCERTAINTY QUANTIFICATION

Published in International journal for uncertainty quantification (01.01.2021)

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ON TRANSFER LEARNING OF NEURAL NETWORKS USING BI-FIDELITY DATA FOR UNCERTAINTY PROPAGATION

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DIMENSIONALITY REDUCTION FOR COMPLEX MODELS VIA BAYESIAN COMPRESSIVE SENSING

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STABILITY AND CONVERGENCE OF SOLUTIONS TO STOCHASTIC INVERSE PROBLEMS USING APPROXIMATE PROBABILITY DENSITIES

Published in International journal for uncertainty quantification (01.01.2025)

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MORE POWERFUL HSIC-BASED INDEPENDENCE TESTS, EXTENSION TO SPACE-FILLING DESIGNS AND FUNCTIONAL DATA

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UNCERTAINTY QUANTIFICATION AND GLOBAL SENSITIVITY ANALYSIS OF SEISMIC FRAGILITY CURVES USING KRIGING

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MFNets: MULTI-FIDELITY DATA-DRIVEN NETWORKS FOR BAYESIAN LEARNING AND PREDICTION

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SENSITIVITY ANALYSES OF A MULTIPHYSICS LONG-TERM CLOGGING MODEL FOR STEAM GENERATORS

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COMPUTATION OF SOBOL INDICES IN GLOBAL SENSITIVITY ANALYSIS FROM SMALL DATA SETS BY PROBABILISTIC LEARNING ON MANIFOLDS

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EFFICIENT TREATMENT OF THE MODEL ERROR IN THE CALIBRATION OF COMPUTER CODES: THE COMPLETE MAXIMUM A POSTERIORI METHOD

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A FILTERED MULTILEVEL MONTE CARLO METHOD FOR ESTIMATING THE EXPECTATION OF CELL-CENTERED DISCRETIZED RANDOM FIELDS

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BAYESIAN PARAMETER INFERENCE FOR PARTIALLY OBSERVED DIFFUSIONS USING MULTILEVEL STOCHASTIC RUNGE-KUTTA METHODS

Published in International journal for uncertainty quantification (2025)

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BAYESIAN CALIBRATION WITH ADAPTIVE MODEL DISCREPANCY

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GRADIENT-BASED STOCHASTIC OPTIMIZATION METHODS IN BAYESIAN EXPERIMENTAL DESIGN

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A BAYESIAN NEURAL NETWORK APPROACH TO MULTI-FIDELITY SURROGATE MODELING

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EMBEDDED MULTILEVEL MONTE CARLO FOR UNCERTAINTY QUANTIFICATION IN RANDOM DOMAINS

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