论文标题
热等离子体模拟的深度学习：以1-D ARC模型为例
Deep learning for thermal plasma simulation: solving 1-D arc model as an example
论文作者
论文摘要
数值建模是理解热等离子体在各种工业应用中的行为的重要方法。我们提出了一种深度学习方法，用于求解热等离子体模型中的部分微分方程。在这种方法中，深层馈送神经网络被构建为替代模型的解决方案。损失函数旨在测量神经网络与描述热等离子体的方程之间的差异。通过最小化此损失函数来获得良好的神经网络。我们通过求解由三种情况组成的1-D ARC衰减模型来证明这种深度学习方法的力量：固定弧，瞬态弧而不考虑径向速度，并分别具有径向速度的瞬态弧。结果表明，深神经网络具有表达描述热等离子体的微分方程的出色能力。这可以为我们带来一个用于热等离子体建模的新的和前瞻性的数值工具。
Numerical modelling is an essential approach to understanding the behavior of thermal plasmas in various industrial applications. We propose a deep learning method for solving the partial differential equations in thermal plasma models. In this method a deep feed-forward neural network is constructed to surrogate the solution of the model. A loss function is designed to measure the discrepancy between the neural network and the equations describing thermal plasmas. A good neural network is obtained by minimizing this loss function. We demonstrate the power of this deep learning method by solving a 1-D arc decaying model which is consist of three cases: stationary arc, transient arc without considering radial velocity, and transient arc with radial velocity respectively. The results show that the deep neural networks have excellent ability to express the differential equations describing thermal plasmas. This could bring us a new and prospective numerical tool for thermal plasma modelling.
