@inproceedings{cal-fae-moe-20-aa-strudyn,
  author = {Callens, Robin R. P. and Faes, Matthias G. R. and Moens, David},
  title = {Local Interval Fields for Spatial Inhomogeneous Uncertainty Modelling in Structural Dynamics},
  booktitle = {Proceedings of the International Conference on Uncertainty in Structural Dynamics},
  location = {Leuven, BE},
  year = 2020,
  month = sep,
  pages = {1-14},
  note = {no DOI? See also [cal-fae-moe-20-aa-inhom], [cal-fae-moe-21-aa-nonstat].},
  comment = {Application of validated numerics to finite element analysis. Mentions AA but does not use it?  Uses ``interval fields''.} 
  abstract = {Interval fields have been introduced to model spatial uncertainty in Finite Element Models when the available data is insufficient to build representative probabilistic models. However, they are limited to modelling global non-stationary uncertainty and hence cannot model local non-stationary uncertainty. This is typically occurring in specific regions of a component or a structure which is produced with,e.g., casting, welding, drawing. This paper presents a more efficient local interval field approach to model the local uncertainty under scarce data. The method is based on the concept of explicit interval fields and aims to develop an alternative approach for the commonly applied inverse distance weighting approach for the generation of the basis functions. In this paper the method is applied on a two-dimensional spatial uncertainty case with a specific focus on dynamics. The paper compares the introduced local interval field approach with inverse distance weighting from a numerical and application point of view.}
}