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預告:Weak Adversarial Networks (WAN): A Deep Learning Framework for Solving High Dimensional Inverse Problem

來源 : 理學院     作者 : 理學院     時間 : 2021-06-30     

時間:6月30日(周三)14:00

地點:勤園21號樓304室

主講人:臧耀華

內容簡介:We present a weak adversarial network approach to numerically solve a class of inverse problems, including electrical impedance tomography. The weak formulation of the PDE for the given inverse problem is leveraged, where the solution and the test function are parameterized as deep neural networks. Then, the weak formulation and the boundary conditions induce a minimax problem of a saddle function of the network parameters. As the parameters are alternatively updated, the network gradually approximates the solution of the inverse problem. Theoretical justifications are provided on the convergence of the proposed algorithm. The proposed method is completely mesh-free without any spatial discretization, and is particularly suitable for problems with high dimensionality and low regularity on solutions. Numerical experiments on a variety of test inverse problems demonstrate the promising accuracy and efficiency of this approach. This presentation is based on the joint work with Gang Bao (Zhejiang U.), Xiaojing Ye (Georgia State U.) and Haomin Zhou (Georgia Tech.)

主講人簡介:臧耀華,2015年在吉林大學取得數學學士學位,2015年至今在浙江大學數學科學學院學習,2018.09-2019.09期間在美國佐治亞理工學院進行訪問。主要研究興趣為基於深度學習的偏微分方程正問題和反問題求解算法,機器學習算法在高維最優控製領域中的應用等。


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地址:浙江省杭州市餘杭塘路2318號
郵編:311121 公安備案號:33011002011919
浙ICP備11056902號
版權所有 © 雷竞技raybet