Special Issue "Modeling and Optimization of Complex Engineering Systems under Uncertainties"

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Engineering Mathematics".

Deadline for manuscript submissions: 30 November 2023 | Viewed by 608

Special Issue Editors

Dr. Debiao Meng
E-Mail Website
Guest Editor
School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: uncertainty-based design and optimization; reliability analysis
Dr. Shui Yu
E-Mail Website
Guest Editor
School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Interests: time-dependent reliability-based robust design optimization

Special Issue Information

Dear Colleagues,

In engineering problems, complex systems usually consist of many subsystems. The coupled disciplines represented by these subsystems are interrelated. Additionally, multi-source mixed uncertainties are accompanied by the transmission and accumulation of coupled information in complex systems. Therefore, the modeling and design processes of complex systems in engineering problems are often time-consuming and inefficient. Moreover, the reliability and safety of system performance cannot be completely guaranteed.

To tackle the above challenges, the development of advanced modeling technology and a design optimization algorithm of complex engineering systems considering mixed uncertainties is necessary. On the one hand, the continuing development of engineering systems makes it difficult for existing modeling methods to efficiently address all new problems. On the other hand, because of the limited time and cost, designers have to select an appropriate method among various design and optimization methods to solve their problems. Consequently, newer uncertainty-based design and optimization methods should be developed to provide greater options for engineering designers.

The aim of this Special Issue is to establish an academic forum between experts and scholars and come to an agreement regarding the current state of this research field; draw a roadmap of where research is headed, highlight issues, and discuss their possible solutions; and provide the data, models and tools necessary for performing complex system modeling and a multidisciplinary design optimization algorithm considering mixed uncertainties. Potential topics include, but are not limited to:

  • System modeling;
  • Multidisciplinary design optimization;
  • System reliability and risk assessment;
  • Structural safety;
  • Interval and fuzzy mathematics;
  • Structural analysis;
  • Optimization problem and computational methods;
  • Information fusion;
  • Fault diagnosis;
  • Probabilistic physics of failure;
  • Uncertainty-based design optimization;
  • Uncertainty quantification and propagation;
  • Performance degradation modeling and analysis.

Dr. Debiao Meng
Dr. Shui Yu
Guest Editors

Manuscript Submission Information

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Keywords

  • Complex systems
  • Reliability
  • Control
  • Advanced modeling technology
  • Design optimization
  • Uncertainty-based design
  • Optimization methods
  • Uncertainty quantification
  • Fault diagnosis

Published Papers (1 paper)

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Research

Article
A Robust Learning Methodology for Uncertainty-Aware Scientific Machine Learning Models
Mathematics 2023, 11(1), 74; https://doi.org/10.3390/math11010074 - 25 Dec 2022
Viewed by 285
Abstract
Robust learning is an important issue in Scientific Machine Learning (SciML). There are several works in the literature addressing this topic. However, there is an increasing demand for methods that can simultaneously consider all the different uncertainty components involved in SciML model identification. [...] Read more.
Robust learning is an important issue in Scientific Machine Learning (SciML). There are several works in the literature addressing this topic. However, there is an increasing demand for methods that can simultaneously consider all the different uncertainty components involved in SciML model identification. Hence, this work proposes a comprehensive methodology for uncertainty evaluation of the SciML that also considers several possible sources of uncertainties involved in the identification process. The uncertainties considered in the proposed method are the absence of a theory, causal models, sensitivity to data corruption or imperfection, and computational effort. Therefore, it is possible to provide an overall strategy for uncertainty-aware models in the SciML field. The methodology is validated through a case study developing a soft sensor for a polymerization reactor. The first step is to build the nonlinear model parameter probability distribution (PDF) by Bayesian inference. The second step is to obtain the machine learning model uncertainty by Monte Carlo simulations. In the first step, a PDF with 30,000 samples is built. In the second step, the uncertainty of the machine learning model is evaluated by sampling 10,000 values through Monte Carlo simulation. The results demonstrate that the identified soft sensors are robust to uncertainties, corroborating the consistency of the proposed approach. Full article
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