Special Issue of a Springer journal "Archive of Applied Mechanics"

on

Numerical analysis and design of structures including polymorphic uncertain data

edited by

Michael Kaliske, Technische Universitaet Dresden, Germany

Vladik Kreinovich, The University of Texas at El Paso, USA

Numerical simulation of structures is currently often characterized by deterministic methods. Deterministic models of the reality indicate preciseness and safety, while, on contrary all available data and information are characterized by various types of uncertainty (variability, imprecision, incompleteness), which cannot be neglected. Polymorphic uncertainty comprehends all of these types of uncertainty and their combinations. The main focus of the special issue is the presentation of methods for the numerical analysis and design of structures under consideration of polymorphic data uncertainty.

Engineering solutions are characterized by inherent robustness and flexibility as essential features for a faultless life of structures under uncertain and changing conditions. Implementing these features in a system requires comprehensive consideration of uncertainty in the model parameters, in environmental and human imposed loads as well as other types of intrinsic and epistemic uncertainties. Numerical design of structures should be robust with respect to (spatial and time dependent) uncertainties inherently present in material characteristics, boundary conditions etc. This requires in turn a reliable numerical analysis, assessment and prediction of the lifecycle of structures, taking explicitly into account the effect of unavoidable uncertainties (limited or dubious information, human factors, subjectivity and experience, linguistic assessment, imprecise measurement, unclear physics etc.).

Recent developments of numerical methods in the field of "real world" engineering problems, which include a comprehensive consideration of uncertainty and associated efficient analysis techniques are requested. Contributions regarding respective advanced methods, such as stochastic analysis, interval analysis, fuzzy analysis, polymorphic uncertainty quantification, surrogate modeling, multi-fidelity simulation and high performance computing are explicitly invited. These may address the incorporation of dependencies within and between uncertain quantities as well as uncertainty models for spatial and temporal dependent quantities

At https://www.springer.com/journal/419, you can find the submission system of the publisher. Please do not forget to indicate the special issue when submitting. The deadline for submissions is June 30, 2023.

We are looking forward to receiving your manuscripts.

Michael Kaliske michael.kaliske@tu-dresden.de<mailto:michael.kaliske@tu-dresden.de> and Vladik Kreinovich vladik@utepo.edu<mailto:vladik@utepo.edu>