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Bayesian Calibration and Uncertainty Quantification for a Physics-Based Precipitation Model of Nickel–Titanium Shape-Memory Alloys
14 references·1 citing·34 second hop
Bayesian Calibration and Uncertainty Quantification for a Physics-Based Precipitation Model of Nickel–Titanium Shape-Memory Alloys (2017)Bayesian Calibration and Uncertainty Quan…Hierarchical Modeling and Analysis for Spatial Data (2014)Hierarchical Modeling and Ana…Variance Components and Generalized Sobol' Indices (2013)Variance Components and Gener…Uncertainty Quantification: Theory, Implementation, and Applications (2013)Uncertainty Quantification: T…Uncertainty Quantification in Multiscale Simulation of Materials: A Prospective (2013)Uncertainty Quantification in…Quantification of Model Uncertainty: Calibration, Model Discrepancy, and Identifiability (2012)Quantification of Model Uncer…Improving Identifiability in Model Calibration Using Multiple Responses (2012)Improving Identifiability in …Handbook of Spatial Statistics (2010)Handbook of Spatial StatisticsA Bayesian calibration approach to the thermal problem (2008)A Bayesian calibration approa…Calibration, validation, and sensitivity analysis: What's what (2006)Calibration, validation, and …Bayesian analysis of computer code outputs: A tutorial (2006)Bayesian analysis of computer…Combining Field Data and Computer Simulations for Calibration and Prediction (2004)Combining Field Data and Comp…Quantifying the uncertainty about the half‐life of deviations from PPP (2002)Quantifying the uncertainty a…Interpolation of Spatial Data: Some Theory for Kriging (2000)Interpolation of Spatial Data…Statistics for Spatial Data. (1993)Statistics for Spatial Data.Uncertainty quantification of the parameters and predictions of a phenomenological constitutive model for thermally induced phase transformation in Ni–Ti shape memory alloys (2019)Uncertainty quantification of…Empirical Model Building and Response Surfaces (1988)STATISTICS FOR SPATIAL DATA (1992)A better understanding of model updating strategies in validating engineering models (2008)Toward a Better Understanding of Model Validation Metrics (2011)Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian process, Part 1: Theory (2018)Design and Analysis of Computer Experiments (1989)A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (2000)Reliability Analysis With Monte Carlo Simulation and Dependent Kriging Predictions (2016)A Tutorial on Bayesian Inference to Identify Material Parameters in Solid Mechanics (2019)Modeling, analysis, and optimization under uncertainties: a review (2021)Minimax and maximin distance designs (1990)A Framework for Validation of Computer Models (2007)Choosing the Sample Size of a Computer Experiment: A Practical Guide (2009)The Design and Analysis of Computer Experiments (2003)Probabilistic Sensitivity Analysis of Complex Models: A Bayesian Approach (2004)A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models (2012)DiceKriging , DiceOptim : Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization (2012)An overview of methods to evaluate uncertainty of deterministic models in decision support (2014)Spatio-temporal modeling of particulate matter concentration through the SPDE approach (2012)Spatial and spatio-temporal models with R-INLA (2013)Cluster-robust inference: A guide to empirical practice (2022)Gaussian Predictive Process Models for Large Spatial Data Sets (2008)Spatial Panel Econometrics (2008)An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach (2011)Selection of model discrepancy priors in Bayesian calibration (2014)Uncertainty quantification in multiscale simulation of woven fiber composites (2018)Review of statistical model calibration and validation—from the perspective of uncertainty structures (2019)Model-Based Geostatistics (1998)Use of Kriging Models to Approximate Deterministic Computer Models (2005)Methods to account for spatial autocorrelation in the analysis of species distributional data: a review (2007)Cross‐validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure (2016)A unified framework for stochastic optimization (2018)Modern Monte Carlo methods for efficient uncertainty quantification and propagation: A survey (2020)A state-of-the-art review on uncertainty analysis of rotor systems (2022)
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Cited by

  • 2019Uncertainty quantification of the parameters and predictions of a phenomenological constitutive model for thermally induced phase transformation in Ni–Ti shape memory alloys

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12 cited