| dc.description.abstract | With this special section issue, we hope to illustrate in which direction the research of nonprobabilistic research is moving. From the included papers, it is clear that nonprobabilistic and hybrid methods are highly suitable to account for combinations of epistemic and aleatory uncertainty in the definition of (the parameters of) a numerical model. Further, more and more approaches are being developed to effectively deal with subproblems in the definition, modeling and propagation of those models. In this context, the biggest challenge might just as well be selecting the most appropriate modeling technique from the plethora of available methods, given the constraints on the available data. Further, translating these methods toward practical engineering cases, including the incorporation of realistic data sources, remains in many cases an open issue, be it that the data are scarce, missing, corrupted, vague, ambiguous, subjective, diffuse or consist , for instance, of measurements or (potentially conflicting) expert opinions. These data-related challenges are often coined under the mnemonic “MUSIC-3X”: multivariate, uncertain, unique, sparse, incomplete, corrupted and 3D-spatially variable. This term was originally introduced to denote geotechnical data [2], but is applicable to almost all fields of modern-day engineering that are faced with real data sources, be it offshore, wind, mechanical, infrastructural or energy engineering, as , for instance, also evidenced by multidisciplinary UQ challenges such as the 2019 NASA Langley UQ Challenge on optimization under uncertainty. | |
