Title: Engineering Artificial Intelligence: Framework, Challenges, and Future Direction Show Chinese title

Title: 工程人工智能：框架、挑战与未来方向

Abstract: 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. Show Chinese abstract

Abstract: 在过去十年里，人工智能（AI）和机器学习（ML）在工程领域的应用日益流行，展示了它们在数据驱动环境中的潜力。然而，工程问题的复杂性和多样性通常需要开发特定领域的AI方法，而这些方法的发展过程往往受到缺乏系统性方法论、可扩展性和鲁棒性的阻碍。 为了解决这一差距，本文介绍了“ABCDE”作为工程AI的关键要素，并提出了一种统一且系统的工程AI生态系统框架，包括八个基本层，以及其属性、目标和应用，以指导针对特定工程需求的AI解决方案的开发与部署。此外，还探讨了关键挑战，并突出了八个未来研究方向。 通过提供全面的视角，本文旨在推动AI的战略实施，促进下一代工程AI解决方案的发展。