The DOI matches its Crossref record; 320 of the 320 references it cites were found in a registry or an index.
The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support
Open accessAbstract
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.
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Cited by
Toward SALib 2.0: Advancing the accessibility and interpretability of global sensitivity analyses
Iwanaga, T., Usher, W., & Herman, J. (2022) · Socio-Environmental Systems Modelling · doi:10.18174/sesmo.18155 · Cited by 367
Self-citationLafferty, D. C., & Sriver, R. L. (2023) · npj Climate and Atmospheric Science · doi:10.1038/s41612-023-00486-0 · Cited by 139
Sivanesan, U., Wu, K., McInnes, M. D. F., Dhindsa, K., Salehi, F., & van der Pol, C. B. (2023) · Canadian Association of Radiologists Journal · doi:10.1177/08465371221134056 · Cited by 19
Time-integrated radiation risk metrics and interpopulation variability of survival
Ulanowski, A., Ban, N., Ozasa, K., Rühm, W., Semones, E., Shavers, M., & Vaillant, L. (2024) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2023.08.002 · Cited by 4
Metadata
- Authors
- Razavi, S., Jakeman, A., Saltelli, A., Prieur, C., Iooss, B., Borgonovo, E., Plischke, E., Lo Piano, S., Iwanaga, T., Becker, W., Tarantola, S., Guillaume, J. H. A., Jakeman, J., Gupta, H., Melillo, N., Rabitti, G., Chabridon, V., Duan, Q., Sun, X., Smith, S., Sheikholeslami, R., Hosseini, N., Asadzadeh, M., Puy, A., Kucherenko, S., & Maier, H. R.
- Venue
- Environmental Modelling & Software
- Publisher
- Elsevier BV
- Published
- 2021-03
- Volume
- 137
- Pages
- 104954
- ISSN
- 1364-8152 (ISSN-L)
- OpenAlex
- W3112134271
- Cited by
- 628
- References
- 320
- FWCI
- 38.58
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Razavi, S., Jakeman, A., Saltelli, A., Prieur, C., Iooss, B., Borgonovo, E., Plischke, E., Lo Piano, S., Iwanaga, T., Becker, W., Tarantola, S., Guillaume, J. H. A., Jakeman, J., Gupta, H., Melillo, N., Rabitti, G., Chabridon, V., Duan, Q., Sun, X., . . . Maier, H. R. (2021). The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support. Environmental Modelling & Software. https://doi.org/10.1016/j.envsoft.2020.104954
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
