MPIPN:用于求解参数声学结构系统的多物理信息点网

IF 8.7 2区 工程技术 Q1 Mathematics
Chu Wang, Jinhong Wu, Yanzhi Wang, Zhijian Zha, Qi Zhou
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引用次数: 0

摘要

机器学习被用于求解由一般非线性偏微分方程(PDE)支配的物理系统。然而,复杂的多物理场系统(如声-结构耦合)通常由一系列包含可变物理量的 PDEs 描述,这些 PDEs 被称为参数系统。目前缺乏解决由涉及显性和隐性量的 PDEs 所支配的参数系统的策略。本文提出了一种基于深度学习的多物理信息点网(MPIPN),用于求解参数声学结构系统。首先,MPIPN 引入了增强型点云架构,该架构包含计算域的显式物理量和几何特征。然后,MPIPN 从重建的点云中提取局部和全局特征,分别作为参数系统求解标准的一部分。此外,通过编码技术嵌入隐式物理量,作为求解标准的另一部分。最后,所有表征参数系统的求解标准被合并成独特的序列,作为 MPIPN 的输入,而 MPIPN 的输出则是系统的解。针对相应的计算域,提出的框架通过自适应物理信息损失函数进行训练。该框架可通用于处理新的系统参数条件。通过将 MPIPN 应用于求解受亥姆霍兹方程支配的稳定参数声学-结构耦合系统,验证了 MPIPN 的有效性。还实施了一项烧蚀实验,利用少数监督数据证明了物理信息影响的有效性。在声学-结构系统的恒定参数条件和可变参数条件组合下,所提出的方法在所有计算域都能获得合理的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

MPIPN: a multi physics-informed PointNet for solving parametric acoustic-structure systems

MPIPN: a multi physics-informed PointNet for solving parametric acoustic-structure systems

Machine learning is employed for solving physical systems governed by general nonlinear partial differential equations (PDEs). However, complex multi-physics systems such as acoustic-structure coupling are often described by a series of PDEs that incorporate variable physical quantities, which are referred to as parametric systems. There are lack of strategies for solving parametric systems governed by PDEs that involve explicit and implicit quantities. In this paper, a deep learning-based Multi Physics-Informed PointNet (MPIPN) is proposed for solving parametric acoustic-structure systems. First, the MPIPN introduces an enhanced point-cloud architecture that encompasses explicit physical quantities and geometric features of computational domains. Then, the MPIPN extracts local and global features of the reconstructed point-cloud as parts of solving criteria of parametric systems, respectively. Besides, implicit physical quantities are embedded by encoding techniques as another part of solving criteria. Finally, all solving criteria that characterize parametric systems are amalgamated to form distinctive sequences as the input of the MPIPN, whose outputs are solutions of systems. The proposed framework is trained by adaptive physics-informed loss functions for corresponding computational domains. The framework is generalized to deal with new parametric conditions of systems. The effectiveness of the MPIPN is validated by applying it to solve steady parametric acoustic-structure coupling systems governed by the Helmholtz equations. An ablation experiment has been implemented to demonstrate the efficacy of physics-informed impact with a minority of supervised data. The proposed method yields reasonable precision across all computational domains under constant parametric conditions and changeable combinations of parametric conditions for acoustic-structure systems.

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来源期刊
Engineering with Computers
Engineering with Computers 工程技术-工程:机械
CiteScore
16.50
自引率
2.30%
发文量
203
审稿时长
9 months
期刊介绍: Engineering with Computers is an international journal dedicated to simulation-based engineering. It features original papers and comprehensive reviews on technologies supporting simulation-based engineering, along with demonstrations of operational simulation-based engineering systems. The journal covers various technical areas such as adaptive simulation techniques, engineering databases, CAD geometry integration, mesh generation, parallel simulation methods, simulation frameworks, user interface technologies, and visualization techniques. It also encompasses a wide range of application areas where engineering technologies are applied, spanning from automotive industry applications to medical device design.
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