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Spatio-Temporal Classification of Lung Ventilation Patterns using 3D EIT Images: A General Approach for Individualized Lung Function Evaluation
A simple convolutional neural network is used for classification.
Deep-Learning-Empowered Inverse Design for Freeform Reconfigurable Metasurfaces
Recently, artificial neural network empowered inverse design for metasurfaces has been developed that can design on-demand meta-atoms with diverse shapes and high performance, where the design process based on artificial intelligence is fast and automatic.
Physics Embedded Machine Learning for Electromagnetic Data Imaging
To benefit from prior knowledge in big data and the theoretical constraint of physical laws, physics embedded ML methods for EM imaging have become the focus of a large body of recent work.
Neural Born Iteration Method For Solving Inverse Scattering Problems: 2D Cases
In this paper, we propose the neural Born iterative method (NeuralBIM) for solving 2D inverse scattering problems (ISPs) by drawing on the scheme of physics-informed supervised residual learning (PhiSRL) to emulate the computing process of the traditional Born iterative method (TBIM).
Solving Combined Field Integral Equation With Deep Neural Network for 2-D Conducting Object
Abstract—Solving the combined field integral equation (CFIE) for the large-scale scattering problem is computationally expensive.
