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<p>Artificial intelligence (AI) has recently achieved significant breakthroughs in its ability to learn from existing data and generate innovative solutions, leading to transformative impacts in engineering and product development. The integration of AI into design processes is not merely enhancing efficiency but also reshaping traditional paradigms of creativity, decision-making, and life&#xa0;cycle management. Despite these advances, most studies have concentrated on individual algorithms, domains, or functional roles, while lacking a coherent framework to classify problems and guide algorithm selection systematically. This review addresses this gap by introducing a four-dimensional framework for AI-driven design problems encompassing the role of AI, design stage, problem type, and application field. This survey systematically reviews a wide spectrum of AI technologies and introduces their applications corresponding to&#xa0;the four-dimensional framework. It further provides practical guidance for algorithm selection within each dimension. The utility of this framework is demonstrated through integrated case studies in wind turbine design, showcasing applications such as aerodynamic surrogate modeling using neural networks and reliability-based design optimization. Finally, this study proposes a forward-looking vision for the entire life&#xa0;cycle&#xa0;of AI-driven design, advocating closed-loop, data-informed processes spanning design, manufacturing, operation, and maintenance. Beyond synthesizing the current state of the field, this study establishes a systematic foundation that empowers researchers and practitioners to select, apply, and further develop AI methods in tackling complex design problems.</p>

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AI-driven design: a comprehensive review of its methodologies, applications, and challenges

  • 期刊论文

  • 发表日期：

    2026年4月27日

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  • Jiquan Yan,
  • Weifei Hu,
  • Yifeng Zhao,
  • Tongzhou Zhang,
  • Feng Zhao,
  • Jianhao Fang,
  • Wei Shi,
  • Jianrong Tan

摘要

Artificial intelligence (AI) has recently achieved significant breakthroughs in its ability to learn from existing data and generate innovative solutions, leading to transformative impacts in engineering and product development. The integration of AI into design processes is not merely enhancing efficiency but also reshaping traditional paradigms of creativity, decision-making, and life cycle management. Despite these advances, most studies have concentrated on individual algorithms, domains, or functional roles, while lacking a coherent framework to classify problems and guide algorithm selection systematically. This review addresses this gap by introducing a four-dimensional framework for AI-driven design problems encompassing the role of AI, design stage, problem type, and application field. This survey systematically reviews a wide spectrum of AI technologies and introduces their applications corresponding to the four-dimensional framework. It further provides practical guidance for algorithm selection within each dimension. The utility of this framework is demonstrated through integrated case studies in wind turbine design, showcasing applications such as aerodynamic surrogate modeling using neural networks and reliability-based design optimization. Finally, this study proposes a forward-looking vision for the entire life cycle of AI-driven design, advocating closed-loop, data-informed processes spanning design, manufacturing, operation, and maintenance. Beyond synthesizing the current state of the field, this study establishes a systematic foundation that empowers researchers and practitioners to select, apply, and further develop AI methods in tackling complex design problems.