MeshONet:一个可通用和高效的运营商学习方法,用于结构化网格生成
Jing Xiao1, Xinhai Chen1, Jiaming Peng1
1Laboratory of Digitizing Software for Frontier Equipment, National University of Defense Technology, ChangSha, 410073, China; National Key Laboratory of Parallel and Distributed Computing, National University of Defense Technology, ChangSha, 410073, China; College of Computer Science and Technology, National University of Defense Technology, ChangSha, 410073, China.
概括
MeshONet是一种用于结构化网格生成的新型人工智能方法,提供了显著的加快速度,并且可以在不需要再培训的情况下将其推广到新的几何形状. 这克服了传统和现有的智能方法的局限性.
科学领域:
- 科学计算科学计算
- 计算科学 计算科学
- 在工程领域的人工智能.
背景情况:
- 传统的网格生成方法 (基于TFI,PDE) 面临着平衡效率和质量的挑战.
- 基于物理的智能方法提高了效率,但缺乏对新几何的通用性,需要重新培训.
- 可通用和高效的结构化网格生成对科学计算至关重要.
研究的目的:
- 介绍MeshONet,这是第一个可通用的智能结构网格生成方法.
- 在效率,质量和适应不同几何形状方面解决现有方法的局限性.
- 将网格生成转化为操作员学习问题,可以通过一种新的神经网络架构来解决.
主要方法:
- 网格生成被定义为一个操作员学习问题,具有多个输入/解决函数.
- 建议采用双分支,共享干的神经网络架构来处理功能空间映射.
- 该方法从输入-输出对中学习,以近似用于网格生成的复杂映射.
主要成果:
- 与传统方法相比,MeshONet的速度提高了四个数量级.
- 该方法展示了概括能力,适应未见的几何形状,无需重新训练.
- 保持高质量的网格质量,同时显著提高效率.
结论:
- MeshONet代表了通用智能结构网格生成的突破.
- 拟议的运营商学习方法和网络架构有效地解决了先前的限制.
- 这一进步显著提高了人工智能的实用性和适用性,用于科学计算网格生成.
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