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Elmar Plischke
Researcher at Clausthal University of Technology
Publications - 36
Citations - 1833
Elmar Plischke is an academic researcher from Clausthal University of Technology. The author has contributed to research in topics: Sensitivity (control systems) & Estimator. The author has an hindex of 13, co-authored 34 publications receiving 1324 citations. Previous affiliations of Elmar Plischke include University of Bremen.
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Sensitivity analysis: A review of recent advances
TL;DR: This work investigates in detail the methodological issues concerning the crucial step of correctly interpreting the results of a sensitivity analysis, and presents recent results that permit the estimation of global sensitivity measures by post-processing the sample generated by a traditional Monte Carlo simulation.
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Global sensitivity measures from given data
TL;DR: A design for estimating global sensitivity indices from given data, at the minimum computational cost, is introduced for the identification of the key drivers of uncertainty for the complex computer code developed at the National Aeronautics and Space Administration assessing the risk of lunar space missions.
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The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support
Saman Razavi,Anthony Jakeman,Andrea Saltelli,Clémentine Prieur,Bertrand Iooss,Emanuele Borgonovo,Elmar Plischke,Samuele Lo Piano,Takuya Iwanaga,William E. Becker,Stefano Tarantola,Joseph H. A. Guillaume,John D. Jakeman,Hoshin V. Gupta,Nicola Melillo,Giovanni Rabitti,Vincent Chabridon,Qingyun Duan,Xifu Sun,Stefan Smith,R. Sheikholeslami,R. Sheikholeslami,Nasim Hosseini,Masoud Asadzadeh,Arnald Puy,Arnald Puy,Sergei Kucherenko,Holger R. Maier +27 more
TL;DR: A multidisciplinary group of researchers and practitioners revisit the current status of Sensitivity analysis, and outline research challenges in regard to both theoretical frameworks and their applications to solve real-world problems.
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An effective algorithm for computing global sensitivity indices (EASI)
TL;DR: An algorithm named EASI is presented that estimates first order sensitivity indices from given data using Fast Fourier Transformations and can be used as a post-processing module for pre-computed model evaluations.
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A Common Rationale for Global Sensitivity Measures and Their Estimation.
TL;DR: A unified proof of single-loop consistency that applies to any measure satisfying a common rationale is given, which invokes less restrictive assumptions than heretofore in the literature, allowing for the presence of correlations among model inputs and of categorical variables.