基于强化学习的自学习有限元提取系统

IF 1.7 3区 工程技术 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing Pub Date : 2021-04-21 DOI:10.1017/S089006042100007X
J. Pan, Jingwei Huang, Yunli Wang, G. Cheng, Yong Zeng
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引用次数: 10

摘要

摘要高质量网格的自动生成是CAD/CAE系统的基础。元素提取是一种主要的网格生成方法,因为它能够在域边界周围生成高质量的网格并控制局部网格密度。然而,由于难以在域内部生成令人满意的网格,甚至难以生成完整的网格,它的广泛应用受到了阻碍。元素提取方法的主要挑战是定义元素提取规则,以在具有复杂形状的几何域的边界和内部实现高质量网格。本文提出了一种自学习的元素提取系统FreeMesh-S,该系统可以自动获取鲁棒、高质量的元素提取规则。两个核心组件实现了FreeMesh-S:(1)元素提取规则的三个基元结构,它们是根据任何几何边界形状的边界模式构建的;(2) 一种新的自学习模式,通过结合优势参与者-批评者(A2C)强化学习网络和前馈神经网络(FNN),用于自动定义和细化元素提取规则中包含的参数之间的关系。A2C网络通过使用元素质量作为奖励信号的随机网格元素提取动作来学习网格生成过程,并随着时间的推移产生高质量元素。FNN将A2C生成的网格作为样本进行训练,以快速生成高质量元素。FreeMesh-S在二维四元网格生成中的应用证明了这一点。将FreeMesh-S的网格划分性能与现有的三种常用方法在十个预定义的域边界上进行了比较。实验结果表明,即使开发算法所需的领域知识要少得多,FreeMesh-S在基本指标上也优于这三种方法。FreeMesh-S大大减少了创建高质量网格生成算法所需的时间和专业知识。
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A self-learning finite element extraction system based on reinforcement learning
Abstract Automatic generation of high-quality meshes is a base of CAD/CAE systems. The element extraction is a major mesh generation method for its capabilities to generate high-quality meshes around the domain boundary and to control local mesh densities. However, its widespread applications have been inhibited by the difficulties in generating satisfactory meshes in the interior of a domain or even in generating a complete mesh. The element extraction method's primary challenge is to define element extraction rules for achieving high-quality meshes in both the boundary and the interior of a geometric domain with complex shapes. This paper presents a self-learning element extraction system, FreeMesh-S, that can automatically acquire robust and high-quality element extraction rules. Two central components enable the FreeMesh-S: (1) three primitive structures of element extraction rules, which are constructed according to boundary patterns of any geometric boundary shapes; (2) a novel self-learning schema, which is used to automatically define and refine the relationships between the parameters included in the element extraction rules, by combining an Advantage Actor-Critic (A2C) reinforcement learning network and a Feedforward Neural Network (FNN). The A2C network learns the mesh generation process through random mesh element extraction actions using element quality as a reward signal and produces high-quality elements over time. The FNN takes the mesh generated from the A2C as samples to train itself for the fast generation of high-quality elements. FreeMesh-S is demonstrated by its application to two-dimensional quad mesh generation. The meshing performance of FreeMesh-S is compared with three existing popular approaches on ten pre-defined domain boundaries. The experimental results show that even with much less domain knowledge required to develop the algorithm, FreeMesh-S outperforms those three approaches in essential indices. FreeMesh-S significantly reduces the time and expertise needed to create high-quality mesh generation algorithms.
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来源期刊
CiteScore
4.40
自引率
14.30%
发文量
27
审稿时长
>12 weeks
期刊介绍: The journal publishes original articles about significant AI theory and applications based on the most up-to-date research in all branches and phases of engineering. Suitable topics include: analysis and evaluation; selection; configuration and design; manufacturing and assembly; and concurrent engineering. Specifically, the journal is interested in the use of AI in planning, design, analysis, simulation, qualitative reasoning, spatial reasoning and graphics, manufacturing, assembly, process planning, scheduling, numerical analysis, optimization, distributed systems, multi-agent applications, cooperation, cognitive modeling, learning and creativity. AI EDAM is also interested in original, major applications of state-of-the-art knowledge-based techniques to important engineering problems.
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