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.

灵敏度分析（SA）即将成为数学建模的组成部分。然而，SA的巨大潜在好处尚未完全实现，既可以用于推进人类和自然系统的机械模型和数据驱动的建模，也可以用于决策支持。在这个观点论文中，一个由多学科的研究人员和从业人员组成的小组重新审视了SA的现状，并概述了有关理论框架及其在解决实际问题中的应用方面的研究挑战。讨论了六个值得进一步关注的领域，其中包括：（1）将SA作为一门学科进行结构化和标准化；（2）实现SA在系统建模方面的未开发潜力；（3）解决SA的计算负担；（4）在SA中逐步发展SA。机器学习的背景 （5）阐明SA与不确定性量化之间的关系和作用，以及（6）不断发展SA在决策中的应用。提供了对SA未来的展望，强调了SA必须如何支持各种活动以更好地为科学和社会服务。