Advanced Mathematical Models in Engineering Design Optimization

Dear Colleagues, 

In the rapidly evolving field of engineering design optimization, advanced mathematical models are revolutionizing how complex systems are designed, analyzed, and optimized. This Special Issue explores cutting-edge approaches that integrate sophisticated computational techniques to tackle high-dimensional, computationally expensive problems in engineering. Key advancements include surrogate-assisted design optimization, which leverages approximations to reduce evaluation costs; multi-fidelity surrogate modeling for efficient handling of varying accuracy levels; physics-informed neural networks that embed physical laws into machine learning frameworks for enhanced predictive accuracy; LLM-assisted design optimization, utilizing large language models to automate and innovate design processes; digital twins for real-time optimization through virtual replicas of physical systems; generative design for the automated exploration of innovative forms and structures; and reliability-based design optimization to ensure robustness under uncertainties and probabilistic failures. Additionally, we delve into other hot topics such as robust design optimization, Bayesian optimization under uncertainty, topology optimization for lightweight structures, and hybrid meta-heuristic methods combined with AI. Our goal is to highlight innovative applications that bridge theory and practice, fostering breakthroughs in industries like aerospace, automotive, and manufacturing. 

The potential topics include (but are not limited to) the following:

• Surrogate-assisted design optimization techniques;
• Multi-fidelity surrogate modeling for efficient engineering simulations;
• Physics-informed neural networks in optimization frameworks;
• LLM-based automatic algorithm design for design optimization;
• Digital twins for real-time design optimization and monitoring;
• Generative design algorithms for innovative engineering solutions;
• Reliability-based design optimization under uncertainties;
• Robust design optimization for variability and sensitivity analysis;
• Bayesian optimization and uncertainty quantification in engineering;
• Topology optimization and additive manufacturing integration;
• Hybrid meta-heuristics with machine learning for complex designs;
• Multi-objective optimization in sustainable engineering systems;
• Data-driven approaches for high-dimensional optimization problems.

Dr. Jin Yi
Prof. Dr. Wei Xia
Guest Editors

Manuscript Submission Information

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• design optimization
• multi-fidelity surrogate modeling
• physical-informed neural networks
• generative design
• digital twins