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发布人:曹美玲  发布时间:2020-11-05   浏览次数:10



平台:腾讯会议,会议ID773 297 363,会议密码:1110,会议链接:

题目A neural network scheme for recovering scattering obstacles with limited phaseless far-field data

摘要We consider a geometrical inverse scattering problem of recovering impenetrable obstacles by the associated far-field measurements. The case with phaseless or even limited-aperture far-field data has recently received considerable attentions in the literature due to its practical importance and theoretical challenge. We propose a two-layer sequence-to-sequence neural network that can effectively and efficiently tackle this inverse problem with limited-aperture phaseless data. Superposing the incident waves in generating the training dataset is a crucial ingredient in the architecture of the network. The network state is selectively updated to preserve the specific structure of the underlying data through a gated idea and the use of the long-term memory function from the Long Short-Term Memory (LSTM) neural network. The weights and offsets of the network are updated by optimization algorithms. Both theoretical convergence analysis and extensive numerical experiments are conducted for the proposed method.

报告人简介:尹伟石,长春理工大学副教授,硕士生导师。主要从事数学物理反问题及数值计算、机器学习算法研究。合作主持国家自然科学基金1项,作为主要参与人参与国家自然科学基金2项,省部级项目多项,在《Journal of Computational Physics》、《Electronic Research Archive》、《Alexandria Engineering Journal》、《Advances in Mathematical Physics》等杂志上发表高水平研究论文30余篇。