Special Issue "Methods and Applications of Uncertainty Quantification in Engineering and Science"
A special issue of Algorithms (ISSN 1999-4893).
Deadline for manuscript submissions: 31 January 2020
Dr. Matteo Diez
Research Scientist, National Research Council-Institute of Marine Engineering (CNR-INM), Via di Vallerano 139, 00128 Rome, Italy
Website 1 | Website 2 | E-Mail
Interests: simulation-based design optimization in ship hydrodynamics; fluid-structure interaction and multidisciplinary design optimization; uncertainty quantification and reliability-based robust design optimization; design space dimensionality reduction in shape optimization; dynamic metamodeling and machine learning methods; global derivative-free bio-inspired optimization algorithms
In the last two decades, uncertainty quantification (UQ) methods have received increasing attention, as real-world problems in most science, technology, and engineering areas are affected significantly by uncertainty. When dealing with simulations of complex physical phenomena, uncertainty generally stems from physical modelling, environmental/operating conditions, and to some extent numerical discretization. In the case of problems modeled and solved by partial differential equations (PDEs), this is reflected in the choice of the modeling equations and of the corresponding terms/coefficients, and in the definition of proper initial and boundary conditions, as well as of the computational-domain shape and discretization. While the uncertainty associated with modeling and discretization can be reduced in principle, the uncertainty propagating from environmental and operating conditions is often aleatoric and intrinsic to the problem. Within this framework, solutions to PDEs are no longer sought deterministically, as statistical estimators and/or distributions of relevant simulation outputs are deemed to be a more accurate representation of the real problem under investigation. UQ represents a grand challenge for most problems and users: indeed, it generally requires repeatedly solving the PDE at hand for different values of the random parameters, which might be a very demanding computational task despite the significant development of high-performance computing systems. To overcome the limitations due to the computational cost associated with UQ, several approaches have been investigated by researchers in different areas. The aim of this Special Issue is to collect state-of-the-art research on the topic of computationally-efficient UQ methods and on their applications to complex problems. The Special Issue is organized in collaboration with the Workshop on Frontiers of Uncertainty Quantification in Fluid Dynamics (FrontUQ 2019, https://frontuq19.com/). Contributions from FrontUQ are welcome, as well as papers from other fields of application of UQ and researchers outside the workshop. Relevant topics, methods, and applications are included in (but not limited to) the list below.
Dr. Matteo Diez
Dr. Lorenzo Tamellini
Prof. Dr. Maria Vittoria Salvetti
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- forward propagation of uncertainties
- sensitivity analysis
- inverse problems
- data fusion, data assimilation, and integration with artificial intelligence or machine learning
- metamodeling and machine learning in UQ problems
- adaptive methods
- multi-fidelity and multi-level methods
- dimensionality reduction
- intrusive and non-intrusive methods
- UQ in complex physical and engineering problems
- optimization under uncertainty