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With the development of the automotive market, the complexity of automotive systems has significantly increased. Multidisciplinary Design Analysis and Optimization (MDAO) has been applied to address challenges in analyzing such multidisciplinary problems. The advancement of Artificial Intelligence (AI) technologies is seen as a solution to enhance the capability and efficiency of MDAO. However, current AI applications are not able to align with specific tasks across different phases of system development, such as requirements definition, system design, and system analysis. This gap restricts the transition from AI technology development to industrial practice. This paper aims to define a process that aligns AI algorithm scopes within an integrated analysis framework of Model-Based Systems Engineering (MBSE) and MDAO. This approach facilitates the systematic identification and selection of AI applications. A demonstrated AI-enabling system architecture for general system development processes is established to illustrate how multi-disciplinary problems across requirements, design, and analysis phases can be matched with appropriate AI applications.

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A Feasible AI Application Identification Approach for Enhancing MDAO Processes

  • 会议论文

  • 发表日期：

    2026年

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  • Tianxiao Xu,
  • Nejib Moalla,
  • Mohand Lounes Bentaha,
  • Hazal Aktekin,
  • Giuseppe Cereda,
  • Claudia Agostinelli

摘要

With the development of the automotive market, the complexity of automotive systems has significantly increased. Multidisciplinary Design Analysis and Optimization (MDAO) has been applied to address challenges in analyzing such multidisciplinary problems. The advancement of Artificial Intelligence (AI) technologies is seen as a solution to enhance the capability and efficiency of MDAO. However, current AI applications are not able to align with specific tasks across different phases of system development, such as requirements definition, system design, and system analysis. This gap restricts the transition from AI technology development to industrial practice. This paper aims to define a process that aligns AI algorithm scopes within an integrated analysis framework of Model-Based Systems Engineering (MBSE) and MDAO. This approach facilitates the systematic identification and selection of AI applications. A demonstrated AI-enabling system architecture for general system development processes is established to illustrate how multi-disciplinary problems across requirements, design, and analysis phases can be matched with appropriate AI applications.