Surrogate Modeling for Complex Engineering Design Problems: A Comprehensive Review
Keywords:
Surrogate Modeling, Polynomial Response Surfaces, Gaussian Process Regression, Support Vector Regression (SVR)Abstract
Surrogate modeling has emerged as a powerful tool for solving complex engineering design problems by providing computationally efficient approximations of high-fidelity simulations. In disciplines such as aerospace, automotive, structural engineering, and biomedical applications, high-fidelity models require significant computational resources, making optimization and real-time decision-making challenging. Traditional optimization methods often struggle with the computational burden associated with iterative simulations, necessitating alternative approaches that can reduce computational cost while maintaining accuracy.
Surrogate models, including polynomial response surfaces, Kriging, artificial neural networks (ANNs), Gaussian process regression, radial basis function (RBF) models, and support vector regression (SVR), enable rapid evaluations and facilitate efficient design exploration. These models approximate expensive simulations and enable engineers to perform parametric studies, uncertainty quantification, and multi-disciplinary optimization without the need for exhaustive computations. Additionally, hybrid surrogate modeling approaches, which combine multiple modeling techniques or integrate multi-fidelity simulations, have shown promising results in balancing accuracy and computational efficiency.
This review presents an in-depth discussion of surrogate modeling techniques, their theoretical foundations, practical applications in engineering optimization, and recent advancements in hybrid and adaptive approaches. Special attention is given to the role of machine learning and artificial intelligence in enhancing surrogate model performance, particularly in high-dimensional and nonlinear optimization problems. Furthermore, the challenges associated with surrogate modeling, such as model selection, generalization, error estimation, and robustness, are explored in detail.
Future research directions are identified, including the development of adaptive AI-driven frameworks, automated model refinement techniques, and improved uncertainty quantification methods. The integration of surrogate modeling with digital twins, real-time decision-making systems, and high-performance computing is expected to further advance its capabilities and expand its application across diverse engineering domains. This study aims to provide insights into emerging trends that will shape the next generation of design optimization frameworks, making them more efficient, scalable, and intelligent.
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