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基于KAN与动态上采样器的流场预测模型

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针对流场预测需求,本文提出一种结合科尔莫哥罗夫-阿诺德网络(KAN)与动态上采样器(DySample:Upsampling by Dynamic Sampling)的 KAN耦合模型(KADS),并利用二维菱形翼型数据开展流场数据预测应用.本文改变原始 KAN的激活函数 B-Spline,构建FourierKAN、GRBFKAN、RBFKAN、ChebyKAN等KAN结构,并对其耦合DySample后的性能进行评估.通过与传统的多层感知机(MLP)进行对比发现,以切比雪夫多项式为激活函数的ChebyKAN能以较少的训练时间和次数实现较高的准确率,且在测试时不会出现过拟合的现象.结果表明:本文提出的 KADS模型适用于流场预测分析任务,能够为深度学习流体智能建模提供新的建模方法与思路.
Flow Field Prediction Model Based on KAN and Dynamic Upsample
In order to meet the demand for flow field prediction,this paper proposes KAN coupling model(KADS)combining Kolmogorov-Arnold network(KAN)and dynamic upsample(DySample:Upsampling by Dynamic Sampling),and uses two-dimensional diamond-shaped airfoil data to carry out flow field data prediction applications.In this paper,the activation function of the original KAN B-Spline is changed,and the KAN structures such as FourierKAN,GRBFKAN,RBFKAN,ChebyKAN are constructed,and their performance after coupling with DySample is evaluated.By comparing with the traditional MLP,it is found that ChebyKAN with Chebyshev polynomial as the activation function can achieve high accuracy with less training time and times,and there will be no overfitting during the test.The results show that the KADS model proposed in this paper can be applied to the task of flow field prediction and analysis,and can provide new modeling methods and ideas for the deep learning fluid intelligence modeling task.

Kolmogorov-Arnold networksdynamic upsampleneural networkflow prediction

常绍波、陈泽伟、余建庚、刘子扬、陈刚

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西安交通大学航天航空学院 复杂服役环境重大装备结构强度与寿命全国重点实验室,陕西 西安 710049

航天通信控股集团股份有限公司,浙江 杭州 310009

Kolmogorov-Arnold networks 动态上采样器 神经网络 流场预测

2024

计算物理
中国核学会

计算物理

CSTPCD北大核心
影响因子:0.366
ISSN:1001-246X
年,卷(期):2024.41(6)