Fast and efficient computational electromagnetic simulation is a long-standing challenge. In this work, we propose a data-driven model to solve Poisson’s equation that leverages the learning capacity of deep learning techniques. A deep convolutional neural network is trained to predict the electric potential with different excitations and permittivity distribution in 2D and 3D models. With a careful design of cost function and proper training data generated from finite-difference solvers, the proposed network enables reliable simulation with significant speedup and fairly good accuracy. Numerical experiments show that the same convolutional neural network architecture is effective for both 2D and 3D models, and the average relative prediction error of the proposed convolutional neural network model is less than 3% in both 2D and 3D simulation with significant reduction in computing time compared to the finitedifference solver. This study shows that the deep neural networks have a good learning capacity for numerical simulations. This could help us to build some fast solvers for some computational electromagnetic problems.

Mastering of medical knowledge to human is a lengthy process that typically involves several years of school study and residency training. Recently, deep learning algorithms have shown potential in solving medical problems. Here we demonstrate mastering clinical medical knowledge at certificated-doctor-level via a deep learning framework Med3R, which utilizes a human-like learning and reasoning process. Med3R becomes the first AI system that has successfully passed the written test of National Medical Licensing Examination in China 2017 with 456 scores, surpassing 96.3% human examinees. Med3R is further applied for providing aided clinical diagnosis service based on real electronic medical records. Compared to human experts and competitive baselines, our system can provide more accurate and consistent clinical diagnosis results. Med3R provides a potential possibility to alleviate the severe shortage of qualified doctors in countries and small cities of China by providing computer-aided medical care and health services for patients.