一、题目：

A model-data asymptotic-preserving neural network method based on micro-macro decomposition for gray radiative transfer equations

二、主讲人：

孙文俊

三、摘要：

This talk aims to introduce a model-data asymptotic-preserving neural network (MD-APNN) method to solve the nonlinear gray radiative transfer equations(GRTEs). The system is challenging to be simulated with both the traditional numerical schemes and the vanilla physics-informed neural networks(PINNs) due to the multiscale characteristics. Under the framework of PINNs, we employ a micro-macro decomposition technique to construct a new asymptotic-preserving(AP) loss function, which includes the residual of the governing equations in the micro-macro coupled form, the initial and boundary conditions, the additional constraints and a few labeled data. A number of numerical examples are presented to illustrate the efficiency of MD-APNNs, and particularly, the importance of the AP property in the neural networks for the diffusion dominating problems.

四、主讲人简介：

孙文俊，北京应用物理与计算数学研究所研究员、北京大学应用物理与技术科学中心研究员，主要研究可压缩流体力学方程的数学理论和数值方法、粒子输运方程的数值方法。对辐射输运方程构造了渐近保持的统一气体动理学格式（UGKS），在应用与计算数学领域发表期刊论文30多篇，在惯性约束聚变大型程序研制方面的成果获得军队科技二等奖。

五、邀请人：

施意

六、时间：

3月24日（周五）14:00-15:00

七、地点：

腾讯会议

八、联系人：

施意，联系方式：shiyi@sdu.edu.cn

九、主办：

山东大学数学学院