Topic: Adaptive Surrogate Modeling for Reliability Evaluation and Multidisciplinary Design Optimization

With the continuous enhancement of product performance and quality standards and the introduction of an expanding number of non-standard parts and new materials, modern engineering machinery-related enterprises are increasingly pursuing higher quality and reliable engineering products. Reliability analysis and optimization of products will help confirm their high-quality characteristics throughout the production process. The adaptive surrogate model has demonstrated its effectiveness in solving the problems faced in engineering systems, such as high complexity of performance functions, difficulty in display expression, and huge computational costs. It is widely used in addressing reliability and optimization concerns. This Special Issue aims to explore the development and application of these models in reliability assessment and multidisciplinary design optimization.

The surrogate model method is a model approximation technology based on experimental design for handling multi-variable and complex engineering modeling analysis. Its adaptive process automatically adjusts and supplements model processing parameters and approaches based on the data characteristics of known sample points when investigating the model characteristics of unknown areas. This makes it consistent with the statistical distribution characteristics of the target data to be processed or its structural characteristics. The modeling technique based on the adaptive surrogate model can use a smaller number of samples to predict its failure probability with higher accuracy, improving computing efficiency and significantly reducing computing costs. It has important theoretical and engineering application value in the reliability assessment of complex structures and multidisciplinary design optimization research.

The proposed Special Issue endeavors to build an academic communication platform to promote the innovative development of adaptive surrogate model methods in the fields of reliability assessment and multidisciplinary design optimization. Potential topics include, but are not limited to:

● Modeling methods;

● Adaptive surrogate models;

● Structure integrity;

● Structural reliability;

● Reliability evaluation methods;

● Optimization methods;

● Multiple failure modes;

● Prediction and health management;

● Machine learning;

● Artificial intelligence;

● Heuristic algorithms;

● Reliability-based multidisciplinary design optimization.

Adaptive surrogate model, reliability evaluation, multidisciplinary design optimization, system modeling method, machine learning

31 Dec 2024