论文标题
基于动力学模拟的Euler-Poisson系统的神经网络关闭
A neural network closure for the Euler-Poisson system based on kinetic simulations
论文作者
论文摘要
这项工作涉及等离子体的建模，这些等离子体是带电的粒子流体。多亏了机器倾斜，我们为一维欧拉 - 波森系统构建了一个封闭，有效期有效。这种封闭基于一个称为V-NET的完全卷积神经网络，将整个空间密度，平均速度和温度的输入作为输入，并预测为输出整个热量通量。它是从来自弗拉索夫 - 波森方程的动力学模拟的数据中学到的。数据生成和预处理的设计旨在确保在所选的Knudsen数字（参数化碰撞方案）上几乎均匀的精度。最后，进行了几项数值测试，以评估整个管道的有效性和灵活性。
This work deals with the modeling of plasmas, which are charged-particle fluids. Thanks to machine leaning, we construct a closure for the one-dimensional Euler-Poisson system valid for a wide range of collision regimes. This closure, based on a fully convolutional neural network called V-net, takes as input the whole spatial density, mean velocity and temperature and predicts as output the whole heat flux. It is learned from data coming from kinetic simulations of the Vlasov-Poisson equations. Data generation and preprocessings are designed to ensure an almost uniform accuracy over the chosen range of Knudsen numbers (which parametrize collision regimes). Finally, several numerical tests are carried out to assess validity and flexibility of the whole pipeline.
