From text to tech: Shaping the future of physics-based simulations with AI-driven generative models

被引：4
|
作者：
Alexiadis, Alessio [1 ]
Ghiassi, Bahman [2 ]
机构：
[1] Univ Birmingham, Sch Chem Engn, Birmingham B15 2TT, England
[2] Univ Birmingham, Sch Engn, Birmingham B15 2TT, England
来源：
RESULTS IN ENGINEERING | 2024年 / 21卷
关键词：
Multiphysics software; Physics-informed machine learning; Computational fluid dynamics software; Coupling large language models with Physics; based simulations; Generative AI in engineering;
D O I：
10.1016/j.rineng.2023.101721
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
This micro-article introduces a method for integrating Large Language Models with geometry/mesh generation software and multiphysics solvers, aimed at streamlining physics-based simulations. Users provide simulation descriptions in natural language, which the language model processes for geometry/mesh generation and physical model definition. Initial results demonstrate the feasibility of this approach, suggesting a future where non-experts can conduct advanced multiphysics simulations by simply describing their needs in natural language, while the code autonomously handles complex tasks like geometry building, meshing, and setting boundary conditions.
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页数：5
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