近年来,将人工神经网络用于求解偏微分方程正反问题的研究发展迅速.在正问题求解上,基于神经网络的Penalty-Free Neural Network-2(PFNN-2)方法可精确逼近问题的初始与本质边界条件,放松对解的光滑性要求,实现比较理想的求解精度(Sheng and Yang,CiCP,2022)[1]在本文中,将结合PFNN-2的特点,将其扩展至偏微分方程参数反演问题当中.为了实现该目标,在原PFNN-2损失函数基础上,引入数据驱动损失项,同时制定了相应的平衡系数自适应策略.在数值实验中以Burgers方程及对流扩散方程中的参数反演为例,对提出的反演方法进行了测试,验证了方法的可行性.本研究扩展了 PFNN-2方法的应用范围.
Abstract
In recent years,the researches on employing artificial neural networks to solve forward and inverse problems involving partial differential equations have developed rapidly.In solv-ing the forward problems,Penalty-Free Neural Network-2(PFNN-2)method can accurately approximate the initial and essential boundary conditions of the problem,relax the smooth-ness requirement about the solution,and achieve satisfactory solution accuracy(Sheng and Yang,CiCP,2022)[1].In this paper,we extend PFNN-2 to the parameter inversion prob-lem of partial differential equation by combining its characteristics.To achieve this goal,a data-driven loss term is introduced on the basis of the original PFNN-2 loss function,and an adaptive strategy for the corresponding balance coefficient is designed.In numerical experi-ments,taking inversions of parameters in Burgers equation and convection-diffusion equation as examples,the proposed inversion method is tested,validating its feasibility.This study extends the application scope of the PFNN-2 method.
维普
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