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Simulation and Sensor Optimization for Multifidelity Dynamics Model Calibration
16 references·0 citing·45 second hop
Simulation and Sensor Optimization for Multifidelity Dynamics Model Calibration (2019)Simulation and Sensor Optimization for Mu…Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization (2018)Survey of Multifidelity Metho…Calibration experimental design considering field response and model uncertainty (2017)Calibration experimental desi…Multi-fidelity approach to dynamics model calibration (2015)Multi-fidelity approach to dy…Global sensitivity analysis-enhanced surrogate (GSAS) modeling for reliability analysis (2015)Global sensitivity analysis-e…Multifidelity approaches for optimization under uncertainty (2014)Multifidelity approaches for …GRADIENT-BASED STOCHASTIC OPTIMIZATION METHODS IN BAYESIAN EXPERIMENTAL DESIGN (2014)GRADIENT-BASED STOCHASTIC OPT…A response surface approach for structural reliability analysis using evidence theory (2014)A response surface approach f…Efficient Global Reliability Analysis for Nonlinear Implicit Performance Functions (2008)Efficient Global Reliability …Blind Kriging: A New Method for Developing Metamodels (2008)Blind Kriging: A New Method f…Construction of response surfaces based on progressive-lattice-sampling experimental designs with application to uncertainty propagation (2003)Construction of response surf…The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations (2002)The Wiener--Askey Polynomial …Neural-network-based reliability analysis: a comparative study (2001)Neural-network-based reliabil…A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code (2000)A Comparison of Three Methods…Response Surface Methodology: Process and Product Optimization Using Designed Experiments (1996)Response Surface Methodology:…Optimal Latin-hypercube designs for computer experiments (1994)Optimal Latin-hypercube desig…Empirical Model Building and Response Surfaces (1988)Empirical Model Building and …Multilevel Monte Carlo Path Simulation (2008)INTRODUCTION TO STOCHASTIC SEARCH AND OPTIMIZATION: ESTIMATION, SIMULATION, AND CONTROL (2012)Further analysis of multilevel Monte Carlo methods for elliptic PDEs with random coefficients (2013)Multifidelity importance sampling (2015)Surrogate-assisted global sensitivity analysis: an overview (2020)Modeling, analysis, and optimization under uncertainties: a review (2021)Statistics for Experimenters (1979)Response Surfaces: Designs and Analyses (1997)Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems (2003)Explaining Fixed Effects: Random Effects Modeling of Time-Series Cross-Sectional and Panel Data (2014)A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (1979)Design and Analysis of Computer Experiments (1989)Orthogonal Column Latin Hypercubes and Their Application in Computer Experiments (1998)A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code (2000)Algorithmic construction of optimal symmetric Latin hypercube designs (2000)An efficient algorithm for constructing optimal design of computer experiments (2004)Stochastic Finite Elements: A Spectral Approach (1991)High-Order Collocation Methods for Differential Equations with Random Inputs (2005)Global sensitivity analysis using polynomial chaos expansions (2007)Adaptive sparse polynomial chaos expansion based on least angle regression (2010)Structural Reliability Methods (1996)An improved adaptive kriging-based importance technique for sampling multiple failure regions of low probability (2014)Rare Event Estimation Using Polynomial-Chaos Kriging (2016)Structural Reliability Analysis and Prediction (2017)Monte Carlo and variance reduction methods for structural reliability analysis: A comprehensive review (2023)Structural reliability under combined random load sequences (1978)Designs for Computer Experiments (1989)Probability, reliability and statistical methods in engineering design (1999)DAKOTA : a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis. 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References

  • 1996Response Surface Methodology: Process and Product Optimization Using Designed Experiments9,540 cited
  • 1988Empirical Model Building and Response Surfaces5,235 cited
  • 2002The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations4,821 cited
  • 2000A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code2,856 cited
  • 2008Efficient Global Reliability Analysis for Nonlinear Implicit Performance Functions1,141 cited
  • 2018Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization1,072 cited
  • 1994Optimal Latin-hypercube designs for computer experiments634 cited
  • 2001Neural-network-based reliability analysis: a comparative study271 cited
  • 2008Blind Kriging: A New Method for Developing Metamodels264 cited
  • 2014Multifidelity approaches for optimization under uncertainty198 cited
  • 2015Global sensitivity analysis-enhanced surrogate (GSAS) modeling for reliability analysis185 cited
  • 2003Construction of response surfaces based on progressive-lattice-sampling experimental designs with application to uncertainty propagation123 cited
  • 2014GRADIENT-BASED STOCHASTIC OPTIMIZATION METHODS IN BAYESIAN EXPERIMENTAL DESIGN113 cited
  • 2014A response surface approach for structural reliability analysis using evidence theory

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72 cited
  • 2015Multi-fidelity approach to dynamics model calibration50 cited
  • 2017Calibration experimental design considering field response and model uncertainty46 cited
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