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<p>The integration of physics-based modelling and data-driven artificial intelligence (AI) has emerged as a transformative paradigm in computational mechanics, This perspective reviews the development and current status of AI-empowered frameworks, including data-driven methods, physics-informed neural networks, and neural operators, While these approaches have demonstrated significant promise, challenges remain in terms of robustness, generalisation, and computational efficiency, We delineate four promising research directions: (1) Modular neural architectures inspired by traditional computational mechanics, (2) physics informed neural operators for resolution-invariant operator learning, (3) intelligent frameworks for multiphysics and multiscale biomechanics problems, and (4) structural optimisation strategies based on physics constraints and reinforcement learning, These directions represent a shift toward foundational frameworks that combine the strengths of physics and data, opening new avenues for the modelling, simulation, and optimisation of complex physical systems.</p>

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Towards the future of physics- and data-guided AI frameworks in computational mechanics

  • 期刊论文

  • 发表日期：

    2025年7月9日

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  • Jinshuai Bai,
  • Yizheng Wang,
  • Hyogu Jeong,
  • Shiyuan Chu,
  • Qingxia Wang,
  • Laith Alzubaidi,
  • Xiaoying Zhuang,
  • Timon Rabczuk,
  • Yi Min Xie,
  • Xi-Qiao Feng,
  • Yuantong Gu

摘要

The integration of physics-based modelling and data-driven artificial intelligence (AI) has emerged as a transformative paradigm in computational mechanics, This perspective reviews the development and current status of AI-empowered frameworks, including data-driven methods, physics-informed neural networks, and neural operators, While these approaches have demonstrated significant promise, challenges remain in terms of robustness, generalisation, and computational efficiency, We delineate four promising research directions: (1) Modular neural architectures inspired by traditional computational mechanics, (2) physics informed neural operators for resolution-invariant operator learning, (3) intelligent frameworks for multiphysics and multiscale biomechanics problems, and (4) structural optimisation strategies based on physics constraints and reinforcement learning, These directions represent a shift toward foundational frameworks that combine the strengths of physics and data, opening new avenues for the modelling, simulation, and optimisation of complex physical systems.