报告人：宋方应（ 福州大学 教授）
报告时间：2024年11月23日（周六） 上午 10：30
报告地点: 集美大学章辉楼442
联系人： 林世敏博士
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报告摘要：
In this talk, we try to use Fourier Neural Operator (FNO) networks for solving reaction-diffusion equations. FNO is a novel framework designed to solve partial differential equations by learning mappings between infinite-dimensional functional spaces. We apply FNO to the Surface Quasi-Geostrophic (SQG) equation and test the model with two significantly different initial conditions: vortex initial conditions and sinusoidal initial conditions. Furthermore, we explore the generalization ability of the model by evaluating its performance when trained on vortex initial conditions and applied to sinusoidal initial conditions. Additionally, we investigated the modes (frequency parameters) used during training, analyzing their impact on the experimental results, and determined the most suitable modes for this study.
报告人简介：
宋方应，男，博士、硕士生导师，现为福州大学数学与统计学院研究员。2014年毕业于厦门大学数学科学学院计算数学专业，同年赴美国Brown University应用数学系做博士后。主要研究方向：分数阶微分方程数值解法、多相流谱方法模拟以及机器学习在求解PDEs中的应用等。在SISC，JCP和CMAME等国际期刊上发表论文10余篇。
