The Future of Sensitivity Analysis: An Essential Discipline for Systems Modelling and Policy Making

Autor: Takuya Iwanaga, Saman Razavi, Hoshin V. Gupta, Clémentine Prieur, Joseph H. A. Guillaume, Elmar Plischke, Xifu Sun, John D. Jakeman, Anthony Jakeman, Holger R. Maier, Bertrand Iooss, Stefan Smith, Vincent Chabridon, Samuele Lo Piano, Nasim Hosseini, Masoud Asadzadeh, Qingyun Duan, R. Sheikholeslami, William E. Becker, Emanuele Borgonovo, Arnald Puy, Stefano Tarantola, Andrea Saltelli, Sergei Kucherenko, Giovanni Rabitti, Nicola Melillo
Přispěvatelé: University of Saskatchewan [Saskatoon] (U of S), Fenner School of Environment and Society, Australian National University (ANU), Universitat Oberta de Catalunya [Barcelona] (UOC), Méthodes d'Analyse Stochastique des Codes et Traitements Numériques (GdR MASCOT-NUM), Centre National de la Recherche Scientifique (CNRS), Mathematics and computing applied to oceanic and atmospheric flows (AIRSEA), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université Grenoble Alpes (UGA)-Laboratoire Jean Kuntzmann (LJK), Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Performance, Risque Industriel, Surveillance pour la Maintenance et l’Exploitation (EDF R&D PRISME), EDF R&D (EDF R&D), EDF (EDF)-EDF (EDF), Bocconi University [Milan, Italy], Clausthal University of Technology (TU Clausthal), University of Reading (UOR), European Commission - Joint Research Centre [Ispra] (JRC), Sandia National Laboratories [Albuquerque] (SNL), Sandia National Laboratories - Corporation, University of Arizona, Department of Electrical, Computer and Biomedical Engineering [Pavia], University of Pavia, Department of Actuarial Mathematics and Statistics [Edinburgh], Heriot-Watt University [Edinburgh] (HWU), Hohai University, University of Oxford [Oxford], University of Manitoba [Winnipeg], Department of Ecology and Evolutionary Biology [Princeton], Princeton University, University of Bergen (UiB), Imperial College London, University of Adelaide, Chercheur indépendant, Università degli Studi di Pavia = University of Pavia (UNIPV), University of Oxford, University of South Australia [Adelaide], School of Environment and Sustainability, Department of Decision Sciences, Bocconi University, The University of Arizona, Tucson, AZ, USA, Università degli Studi di Pavia, COLLEGE OF HYDROLOGY AND WATER RESOURCES HOHAI UNIVERSITY NANJING CHN, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Environmental Change Institute, Imperial College London, Department of Chemical Engineering, School of Civil Environmental and Mining Engineering [Adelaide]
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Environmental Modelling and Software
Environmental Modelling and Software, Elsevier, 2021, 137, pp.1-22. ⟨10.1016/j.envsoft.2020.104954⟩
Environmental Modelling and Software, 2021, 137, pp.104954. ⟨10.1016/j.envsoft.2020.104954⟩
Environmental Modelling and Software, Elsevier, 2021, 137, pp.104954. ⟨10.1016/j.envsoft.2020.104954⟩
Environmental Modelling & Software
ISSN: 1364-8152
Popis: International audience; 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.
Databáze: OpenAIRE
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