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

IF 4.6 2区工程技术 Q1 ENGINEERING, MECHANICAL

Acta Mechanica Sinica Pub Date : 2025-07-09 DOI:10.1007/s10409-025-25340-x

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

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引用次数: 0

Abstract

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.

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面向计算力学中物理和数据引导的AI框架的未来

基于物理的建模和数据驱动的人工智能（AI）的集成已经成为计算力学中的一种变革范式。本观点回顾了人工智能授权框架的发展和现状，包括数据驱动方法、物理信息神经网络和神经算子。尽管这些方法已经显示出巨大的希望，但在鲁棒性、泛化和计算效率方面仍然存在挑战。我们描述了四个有前景的研究方向：(1)受传统计算力学启发的模块化神经架构；(2)为分辨率不变算子学习提供物理信息的神经算子；(3)多物理场和多尺度生物力学问题的智能框架；(4)基于物理约束和强化学习的结构优化策略。这些方向代表着向结合物理和数据优势的基础框架的转变，为建模开辟了新的途径。复杂物理系统的模拟和优化。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Acta Mechanica Sinica 物理-工程：机械

CiteScore

5.60

自引率

20.00%

发文量

1807

审稿时长

4 months

期刊介绍： Acta Mechanica Sinica, sponsored by the Chinese Society of Theoretical and Applied Mechanics, promotes scientific exchanges and collaboration among Chinese scientists in China and abroad. It features high quality, original papers in all aspects of mechanics and mechanical sciences. Not only does the journal explore the classical subdivisions of theoretical and applied mechanics such as solid and fluid mechanics, it also explores recently emerging areas such as biomechanics and nanomechanics. In addition, the journal investigates analytical, computational, and experimental progresses in all areas of mechanics. Lastly, it encourages research in interdisciplinary subjects, serving as a bridge between mechanics and other branches of engineering and the sciences. In addition to research papers, Acta Mechanica Sinica publishes reviews, notes, experimental techniques, scientific events, and other special topics of interest. Related subjects » Classical Continuum Physics - Computational Intelligence and Complexity - Mechanics