Engineering Artificial Intelligence: Framework, Challenges, and Future Direction
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909726020730880 |
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| author | Lee, Jay Su, Hanqi Ji, Dai-Yan Minami, Takanobu |
| author_facet | Lee, Jay Su, Hanqi Ji, Dai-Yan Minami, Takanobu |
| contents | Over the past ten years, the application of artificial intelligence (AI) and machine learning (ML) in engineering domains has gained significant popularity, showcasing their potential in data-driven contexts. However, the complexity and diversity of engineering problems often require the development of domain-specific AI approaches, which are frequently hindered by a lack of systematic methodologies, scalability, and robustness during the development process. To address this gap, this paper introduces the "ABCDE" as the key elements of Engineering AI and proposes a unified, systematic engineering AI ecosystem framework, including eight essential layers, along with attributes, goals, and applications, to guide the development and deployment of AI solutions for specific engineering needs. Additionally, key challenges are examined, and eight future research directions are highlighted. By providing a comprehensive perspective, this paper aims to advance the strategic implementation of AI, fostering the development of next-generation engineering AI solutions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02269 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Engineering Artificial Intelligence: Framework, Challenges, and Future Direction Lee, Jay Su, Hanqi Ji, Dai-Yan Minami, Takanobu Artificial Intelligence Machine Learning Over the past ten years, the application of artificial intelligence (AI) and machine learning (ML) in engineering domains has gained significant popularity, showcasing their potential in data-driven contexts. However, the complexity and diversity of engineering problems often require the development of domain-specific AI approaches, which are frequently hindered by a lack of systematic methodologies, scalability, and robustness during the development process. To address this gap, this paper introduces the "ABCDE" as the key elements of Engineering AI and proposes a unified, systematic engineering AI ecosystem framework, including eight essential layers, along with attributes, goals, and applications, to guide the development and deployment of AI solutions for specific engineering needs. Additionally, key challenges are examined, and eight future research directions are highlighted. By providing a comprehensive perspective, this paper aims to advance the strategic implementation of AI, fostering the development of next-generation engineering AI solutions. |
| title | Engineering Artificial Intelligence: Framework, Challenges, and Future Direction |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2504.02269 |