Toward efficient runs of nonlinear gyrokinetic simulations assisted by a convolutional neural network model recognizing wavenumber-space images
Toward efficient runs of nonlinear gyrokinetic simulations assisted by a convolutional neural network model recognizing wavenumber-space images
复制标题
在识别波数空间图像的卷积神经网络模型的辅助下，实现非线性回旋运动模拟的高效运行
DOI：
10.1088/1741-4326/ac70e8
复制
发表时间：
2022
期刊：
影响因子：
3.3
通讯作者：
T.-H. Watanabe
中科院分区：
文献类型：
--
作者：
E. Narita;M. Honda;S. Maeyama;T.-H. Watanabe
一个基于神经网络的创新模型，识别波数空间图像已被开发，以准确地预测湍流热通量的饱和开始时，即饱和时间，在非线性gyrokinetic模拟。重点研究了扰动分布函数的波数空间图像，它能更好地反映湍流的特性。该模型利用最先进的卷积神经网络模型，能够检测图像之间的微小差异。一旦波数空间图像被馈送到所开发的模型中，它可以快速且几乎完美地分类图像处于非线性陀螺动力学模拟中的湍流演化的哪个阶段：线性和非线性增长阶段和饱和阶段。它还可以预测图像处理的模拟时间，具有非常高的准确性。该模型使我们能够预测的饱和时间的gyrokinetic仿真问题通过喂养的图像在早期阶段的模拟和接收的程度的进展走向饱和。该模型的能力，使得它可以很容易地搜索出一个理想的初始条件，快速进行模拟的饱和阶段。这样的预测模型对于在像Fugaku这样的大型超级计算机上运行长时间仿真是重要的，因为它可以有效地使用计算资源。为了提高将要执行的模拟的预测能力，通过具有不同主要不稳定性的数据来训练几个预测模型。最好的预测器被选择使用基于预先执行的线性稳定性计算的结果，具有低的计算成本。
A neural-network based innovative model recognizing the wavenumber space images has been developed to accurately forecast when the saturation of turbulent heat fluxes commences, ie, the saturation time, in nonlinear gyrokinetic simulations. The wavenumber space images of the perturbed distribution function are focused on, which better represent the characteristics of turbulence. The model exploiting the state-of-the-art convolutional neural network model is capable of detecting minuscule differences between the images. Once the wavenumber space image is fed into the developed model, it can quickly and almost perfectly classify which phase of the turbulence evolution in nonlinear gyrokinetic simulations the image is in: the linearly and nonlinearly growing phases and the saturation phase. It can also predict the simulation time at which the image was processed with significantly high accuracy. The model enables us to forecast the saturation time of the gyrokinetic simulation in question by feeding an image at an early stage of the simulation and receiving the degree of progress toward the saturation. The ability of the model makes it possible to easily search out a desirable initial condition that rapidly conducts the simulation to a saturation phase. Such a pre-prediction model is important for running long time simulations on a large scale supercomputer like Fugaku in view of the efficient use of computational resources. In order to improve the predictive capability for the simulation that is going to be performed, several prediction models are trained by data with different major instabilities. The best predictor is selected to be in use based on the result of the pre-performed linear stability calculation with low computational cost.