Sensitivity Analyses in Safety Assessments for Geological Disposal Facilities, An International Collaboration
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Swiler, Laura Painton↗
Engineering topics
Plischke, Elmar
Publications and source records attributed to Plischke, Elmar.
Sensitivity Analysis Comparisons on Geologic Case Studies: An International Collaboration
Over the past four years, an informal working group has developed to investigate existing sensitivity analysis methods, examine new methods, and identify best practices. The focus is on the use of sensitivity analysis in case studies involving geologic disposal of spent nuclear fuel or nuclear waste. To examine ideas and have applicable test cases for comparison purposes, we have developed multiple case studies. Four of these case studies are presented in this report: the GRS clay case, the SNL shale case, the Dessel case, and the IBRAE groundwater case. We present the different sensitivity analysis methods investigated by various groups, the results obtained by different groups and different implementations, and summarize our findings.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Metamodelling sensitivity approaches versus regression and graphical methods on the basis of Geologic Cases: An International Collaboration.
Abstract not provided.
Swiler, Laura Painton↗
The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support
Sensitivity analysis (SA) is en route to becoming an integral part of mathematical modeling. The tremendous potential benefits of SA are, however, yet to be fully realized, both for advancing mechanistic and data-driven modeling of human and natural systems, and in support of decision making. In this perspective paper, a multidisciplinary group of researchers and practitioners revisit the current status of SA, and outline research challenges in regard to both theoretical frameworks and their applications to solve real-world problems. Six areas are discussed that warrant further attention, including (1) structuring and standardizing SA as a discipline, (2) realizing the untapped potential of SA for systems modeling, (3) addressing the computational burden of SA, (4) progressing SA in the context of machine learning, (5) clarifying the relationship and role of SA to uncertainty quantification, and (6) evolving the use of SA in support of decision making. An outlook for the future of SA is provided that underlines how SA must underpin a wide variety of activities to better serve science and society.
54 ENVIRONMENTAL SCIENCES↗