导电边界包围带电粒子的物理信息神经网络
Physics informed neural network for charged particles surrounded by conductive boundaries
摘要：
在本文中，我们开发了一个新的基于PINN的模型来预测被导电壁包围的点电荷粒子的电势。作为一个结果，建议的物理通知神经网络模型，均方误差和R2得分是小于7%和大于90%的相应的例子模拟，分别。结果与典型的神经网络和随机森林作为一个标准的机器学习算法进行了比较。随机森林模型的R2得分为70%，标准神经网络不能很好地训练。此外，计算时间显着减少相比，有限元求解器。
英文摘要：
In this paper, we developed a new PINN-based model to predict the potential of point-charged particles surrounded by conductive walls. As a result of the proposed physics-informed neural network model, the mean square error and R2 score are less than 7% and more than 90% for the corresponding example simulation, respectively. Results have been compared with typical neural networks and random forest as a standard machine learning algorithm. The R2 score of the random forest model was 70%, and a standard neural network could not be trained well. Besides, computing time is significantly reduced compared to the finite element solver.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
