Robust Resource Allocation for Calibration and Validation Tests

[1]  E. E. Myshetskaya,et al.  Monte Carlo estimators for small sensitivity indices , 2008, Monte Carlo Methods Appl..

[2]  I. Sobola,et al.  Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates , 2001 .

[3]  Sabrina Gaba,et al.  Combined use of local and ANOVA-based global sensitivity analyses for the investigation of a stochastic dynamic model: Application to the case study of an individual-based model of a fish population , 2006 .

[4]  Stefano Tarantola,et al.  Improving Random Balance Designs For The Estimation Of First Order Sensitivity Indices , 2010 .

[5]  Olivier Roustant,et al.  Calculations of Sobol indices for the Gaussian process metamodel , 2008, Reliab. Eng. Syst. Saf..

[6]  Sankaran Mahadevan,et al.  Probabilistic Integration of Validation and Calibration Results for Prediction Level Uncertainty Quantification: Application to Structural Dynamics , 2013 .

[7]  A. Saltelli,et al.  A quantitative model-independent method for global sensitivity analysis of model output , 1999 .

[8]  Peter A. J. Hilbers,et al.  A Bayesian approach to targeted experiment design , 2012, Bioinform..

[9]  Zhen Hu,et al.  Global sensitivity analysis-enhanced surrogate (GSAS) modeling for reliability analysis , 2016 .

[10]  Wei Chen,et al.  Analytical Variance-Based Global Sensitivity Analysis in Simulation-Based Design Under Uncertainty , 2005 .

[11]  J. Oden,et al.  A Posteriori Error Estimation in Finite Element Analysis , 2000 .

[12]  Sankaran Mahadevan,et al.  Integration of model verification, validation, and calibration for uncertainty quantification in engineering systems , 2015, Reliab. Eng. Syst. Saf..

[13]  Sankaran Mahadevan,et al.  Test Resource Allocation in Hierarchical Systems Using Bayesian Networks , 2013 .

[14]  Sankaran Mahadevan,et al.  Uncertainty Quantification and Output Prediction in Multi-level Problems , 2014 .

[15]  Alexander Schrijver,et al.  Theory of linear and integer programming , 1986, Wiley-Interscience series in discrete mathematics and optimization.

[16]  S. Andradóttir,et al.  A Simulated Annealing Algorithm with Constant Temperature for Discrete Stochastic Optimization , 1999 .

[17]  Michael Prange,et al.  Toward efficient computation of the expected relative entropy for nonlinear experimental design , 2012 .

[18]  Anil K. Chopra,et al.  Dynamics of Structures: Theory and Applications to Earthquake Engineering , 1995 .

[19]  Saltelli Andrea,et al.  Global Sensitivity Analysis: The Primer , 2008 .

[20]  Art B. Owen,et al.  Better estimation of small sobol' sensitivity indices , 2012, TOMC.

[21]  H. Wynn,et al.  Maximum entropy sampling and optimal Bayesian experimental design , 2000 .

[22]  Sankaran Mahadevan,et al.  Role of calibration, validation, and relevance in multi-level uncertainty integration , 2016, Reliab. Eng. Syst. Saf..

[23]  Thomas L. Paez,et al.  Sandia National Laboratories Validation Workshop: Structural dynamics application☆ , 2008 .

[24]  A. O'Hagan,et al.  Bayesian calibration of computer models , 2001 .

[25]  Sankaran Mahadevan,et al.  Assessing the Reliability of Computational Models under Uncertainty , 2013 .

[26]  Sankaran Mahadevan,et al.  Relative contributions of aleatory and epistemic uncertainty sources in time series prediction , 2016 .

[27]  Gabriel Terejanu,et al.  Bayesian experimental design for the active nitridation of graphite by atomic nitrogen , 2011, ArXiv.

[28]  Sankaran Mahadevan,et al.  Optimal Selection of Calibration and Validation Test Samples Under Uncertainty , 2014 .

[29]  A. Saltelli,et al.  Importance measures in global sensitivity analysis of nonlinear models , 1996 .

[30]  Sankaran Mahadevan,et al.  Separating the contributions of variability and parameter uncertainty in probability distributions , 2013, Reliab. Eng. Syst. Saf..

[31]  Sankaran Mahadevan,et al.  Global Sensitivity Analysis for System Response Prediction Using Auxiliary Variable Method , 2015 .

[32]  Mahesh D. Pandey,et al.  An effective approximation for variance-based global sensitivity analysis , 2014, Reliab. Eng. Syst. Saf..

[33]  A. O'Hagan,et al.  Bayesian emulation of complex multi-output and dynamic computer models , 2010 .