Physics-Guided Neural Network with Model Discrepancy Based on Upper Troposphere Wind Prediction
Physics-Guided Neural Network with Model Discrepancy Based on Upper Troposphere Wind Prediction
复制标题
基于对流层上层风预报的模型误差物理引导神经网络
DOI：
10.1109/icmla.2019.00078
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
and Masayuki Numao
中科院分区：
文献类型：
--
作者：
Ken-ichi Fukui;Junya Tanaka;Tomohiko Tomita;and Masayuki Numao
在本文中，我们重点关注一种将物理模型集成到神经网络中的方法。这项研究提出了一种可以预测两个组成部分的神经网络，即基于物理模型的输出及其模型差异。为了实现这一目标，我们提出了一种新颖的神经网络架构以及基于目标物理模型设计的相关损失函数。物理模型用作空间行为的正则器，其中神经网络的输出用作中间变量。然后，模型差异被定义为其观测值的残差。我们还提出了一种具有共享网络和非共享网络的网络架构，并且可以通过交替优化来训练神经网络。我们以热风方程为例构建了所提出的对流层上层风预测方法。实验结果表明，与普通卷积神经网络或热风方程相比，该方法可以获得更高的预测精度，并且得到的模型差异体现了风矢量的收敛和发散。
In this paper, we focus on a method that integrates a physical model into a neural network. This study proposes a neural network that can predict two components, namely outputs based on a physical model and its model discrepancy. To achieve such a goal, we propose a novel neural network architecture and associated loss functions designed based on a target physical model. The physical model is used as a regularizer of spatial behavior where output from the neural network is used as an intermediate variable. Then, the model discrepancy is defined as its residual to the observation value. We also propose a network architecture which has Shared and Non-Shared networks, and the neural network can be trained by alternate optimization. We constructed the proposed method with wind prediction in the upper troposphere based on thermal wind equations as an example. The experimental results demonstrate that the proposed method can achieve higher predictive accuracy than normal convolutional neural network or using thermal wind equation, also the obtained model discrepancy expresses convergence and divergence of wind vectors.