Uncertainty Quantification and Entropy Analysis
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".
Deadline for manuscript submissions: 30 September 2025 | Viewed by 83
Special Issue Editor
Interests: stochastic finite element method; stochastic reliability; random composites; probabilistic entropy; wavelet analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Probabilistic methods have gained more usage in the various problems of computational mechanics in recent years and are developing dynamically due to numerous applications in reliability theory. Their contemporary usage with some commercial systems like ANSYS enables relatively easy uncertainty quantification (UQ) in many complex problems of mechanics, including the multiscale modeling of composite and cellular materials, the stochastic dynamics of large-scale structures subjected to earthquake and ocean waves or wind excitations, and also viscous damping defined by the use of fractional derivatives. On the other hand, applications of advanced computer algebra systems with statistical libraries like MAPLE encourages the development of higher-order, more precise, or faster probabilistic computer techniques. Another interesting research avenue is the application of artificial intelligence tools for faster, more automatic, and sometimes even more accurate implementations of stochastic methods, specifically in all of the above cases, where implicit structural responses need to be approximated with a series of numerical experiments. Probabilistic analyses, including Kullback–Leibler probabilistic distancing, serves reliability studies or failure predictions, so that any new numerical approaches for the calculation of reliability indexes and probabilities of failure, specifically in time-dependent case studies, are welcome; in this context, any environmentally uncertain actions are of particular interest.
This Special Issue will be a good opportunity to bring together specialists in probabilistic methods to exchange their new ideas and experience in the areas of probabilistic method development and reliability assessments of existing engineering structures and stochastic structural health monitoring. An important goal is to make UQ and probabilistic entropy analysis more popular amongst researchers developing deterministic models and also PhD students. All contributions related to the use of various probabilistic methods and algorithms for the modeling and solutions of mechanical and civil engineering problems are welcome at this mini symposium. The following topics will be particularly considered:
- The Stochastic Finite Element Method and other similar discrete computer methods;
- The application of artificial intelligence tools for stochastic problem solutions;
- Fractional calculus applications in mechanical problems with uncertainty;
- The development of new mathematical and computer methods in UQ problems;
- Error and convergence analysis in stochastic engineering calculus;
- Reliability assessments of existing large-scale structures;
- Durability prediction of structures subjected to uncertain conditions or aging;
- Structural health monitoring of structures with uncertain parameters;
- The application of probabilistic entropy and distance in engineering computations.
Uncertainty quantification in engineering problems of interest include, but are not restricted to, the following:
- Nonlinear responses of materials (hyper-elasticity and so forth) with some uncertainty;
- Structural analysis with geometrical nonlinearity (including various imperfections);
- Structural optimization of structures exhibiting or subjected to some uncertainties;
- Large-scale structures (like towers, masts, or buildings) or multi-scale materials;
- Soil deformations and soil–structure interactions.
Prof. Dr. Marcin Kamiński
Guest Editor
Manuscript Submission Information
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Keywords
- Monte Carlo simulation
- stochastic perturbation method
- semi-analytical approaches
- Bayesian methods
- polynomial chaos expansion
- reliability assessment
- durability prediction
- probabilistic entropy
- probabilistic distance
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