论文标题
基于物理信息神经网络的低温等离子体模拟：框架和初步应用
Low-temperature plasma simulation based on physics-informed neural networks: frameworks and preliminary applications
论文作者
论文摘要
等离子体模拟是一种重要的，有时是研究血浆行为的方法。在这项工作中，我们提出了两个用于低温等离子体模拟的通用AI驱动框架：系数 - 补充物理学信息信息信息，神经网络（CS-PINN）和Runge-Kutta物理学神经网络（RK-PINN）。 CS-PINN使用神经网络或插值函数（例如样条函数）作为子网，以近似于溶液依赖性系数（例如电子影响横截面，热力学特性，传输系数等）。在此基础上，RK-Pinn在神经网络中结合了隐式runge-kutta形式主义，以实现瞬时等离子体的大量时间预测。 CS-PINN和RK-PINN都学习了从时空空间到方程解决方案的复杂非线性关系映射。基于这两个框架，我们通过四个涵盖血浆动力学和流体建模的病例来证明初步应用。结果验证了CS-PINN和RK-PINN在求解血浆方程方面的性能良好。此外，RK-PINN具有为瞬时等离子体模拟提供良好解决方案的能力，不仅具有较大的时间步长，而且还限制了嘈杂的传感数据。
Plasma simulation is an important and sometimes only approach to investigating plasma behavior. In this work, we propose two general AI-driven frameworks for low-temperature plasma simulation: Coefficient-Subnet Physics-Informed Neural Network (CS-PINN) and Runge-Kutta Physics-Informed Neural Network (RK-PINN). The CS-PINN uses either a neural network or an interpolation function (e.g. spline function) as the subnet to approximate solution-dependent coefficients (e.g. electron-impact cross sections, thermodynamic properties, transport coefficients, et al.) in plasma equations. On the basis of this, the RK-PINN incorporates the implicit Runge-Kutta formalism in neural networks to achieve a large-time-step prediction of transient plasmas. Both CS-PINN and RK-PINN learn the complex non-linear relationship mapping from spatio-temporal space to equation's solution. Based on these two frameworks, we demonstrate preliminary applications by four cases covering plasma kinetic and fluid modeling. The results verify that both CS-PINN and RK-PINN have good performance in solving plasma equations. Moreover, the RK-PINN has ability of yielding a good solution for transient plasma simulation with not only large time step but also limited noisy sensing data.
