Physics-informed neural networks for diffraction tomography

作     者：Amirhossein Saba Carlo Gigli Ahmed B.Ayoub Demetri Psaltis Amirhossein Saba;Carlo Gigli;Ahmed B.Ayoub;Demetri Psaltis

作者机构：École Polytechnique Fédérale de LausanneOptics LaboratoryLausanneSwitzerland 

出 版 物：《Advanced Photonics》 (先进光子学（英文）)

年 卷 期：2022年第4卷第6期

页      面：44-55页

核心收录：

学科分类：070207[理学-光学] 07[理学] 08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 0803[工学-光学工程] 0702[理学-物理学] 

基　　金：the Swiss National Science Foundation(SNSF)under funding number 514481 

主　　题：deep learning physics-informed neural networks scattering three-dimensional imaging optical diffraction tomography 

摘      要：We propose a physics-informed neural network(PINN)as the forward model for tomographic reconstructions of biological *** demonstrate that by training this network with the Helmholtz equation as a physical loss,we can predict the scattered field *** will be shown that a pretrained network can be fine-tuned for different samples and used for solving the scattering problem much faster than other numerical *** evaluate our methodology with numerical and experimental *** PINNs can be generalized for any forward and inverse scattering problem.