Review of empowering computer-aided engineering with artificial intelligence

Abstract

Computer-aided engineering (CAE) is widely used in the industry as an approximate numerical analysis method for solving complex engineering and product structural mechanical performance problems. However, with the increasing complexity of structural and performance requirements, the traditional research paradigm based on experimental observations, theoretical modeling, and numerical simulations faces new scientific problems and technical challenges in analysis, design, and manufacturing. Notably, the development of CAE applications in future engineering is constrained to some extent by insufficient experimental observations, lack of theoretical modeling, limited numerical analysis, and difficulties in result validation. By replacing traditional mathematical mechanics models with data-driven models, artificial intelligence (AI) methods directly use high-dimensional, high-throughput data to establish complex relationships between variables and capture laws that are difficult to discover using traditional mechanics research methods, offering significant advantages in the analysis, prediction, and optimization of complex systems. Empowering CAE with AI to find new solutions to the difficulties encountered by traditional research methods has become a developing trend in numerical simulation research. This study reviews the methods and applications of combining AI with CAE and discusses current research deficiencies as well as future research trends.

Keywords

Artificial intelligence (AI) / Computer-aided engineering (CAE) / Deep learning (DL) / Computational mechanics

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Funding

State Key Laboratory of Structural Analysis for Industrial Equipment(GZ22114)

National Natural Science Foundation of China(Grant No. 12202026)

Research Project of State Key Laboratory of Mechanical System and Vibration(Grant No. MSV202401)

RIGHTS & PERMISSIONS

The Author(s)