»ã±¨±êÌâ (Title)£ºA novel deep convolutional surrogate model with incomplete solve loss for parameterized steady-state diffusion problems£¨²ÎÊý»¯ÎÈ̬À©É¢ÎÊÌâµÄÒ»ÖÖеÄÓµÓв»ÆëÈ«½âËðʧµÄÉî¶È¾í»ý´úÀíÄ£ÐÍ£©

»ã±¨ÈË (Speaker)£ºÕÅÏþƽ ¸±½ÌÊÚ£¨Î人´óѧ£©

»ã±¨¹¦·ò (Time)£º2025Äê7ÔÂ13ÈÕ£¨ÖÜÈÕ£©9:30

»ã±¨µØÖ· (Place)£ºÐ£±¾²¿GJ406

Ô¼ÇëÈË(Inviter)£ºÁõ¶«½Ü

Ö÷°ì²¿ÃÅ£ºÀíѧԺÊýѧϵ

»ã±¨ÌáÒª£º In this talk, we will introduce a novel deep surrogate model that integrates the generalization capabilities of convolutional neural networks (CNNs) with traditional numerical methods to solve parametrized steady-state diffusion problems. We will adopt different strategies to handle linear and nonlinear cases separately. In order to solve linear problems, a novel loss function is designed based on an iterative solver for unsupervised training of the model. To solve nonlinear problems, Picard iterations are integrated into the training strategy for unsupervised model training. Extensive numerical experiments are used to valid our method and massive numerical results have shown that our deep surrogate method is capable to solve various parametrized diffusion problems.