论文标题
复发性神经网络架构搜索地球物理仿真
Recurrent Neural Network Architecture Search for Geophysical Emulation
论文作者
论文摘要
从数据开发替代地球物理模型是大气和海洋建模的关键研究主题，因为与数值模拟方法相关的计算成本很高。研究人员已经开始将广泛的机器学习模型（特别是神经网络）应用于地球物理数据，以进行预测，而无需这些限制。但是，构建用于预测此类数据的神经网络是不平凡的，并且通常需要反复试验。为了解决这些局限性，我们专注于开发基于正交分解的长期短期内存网络（POD-LSTMS）。我们开发了可扩展的神经体系结构搜索，以生成堆叠的LSTM，以预测NOAA最佳插值海面温度数据集中的温度。我们的方法标识了POD-LSTM优于手动设计的变体和基线时间序列预测方法。我们还评估了Argonne领导力计算设施中Theta超级计算机的多达512个英特尔骑士降落节点的不同体系结构搜索策略的可扩展性。
Developing surrogate geophysical models from data is a key research topic in atmospheric and oceanic modeling because of the large computational costs associated with numerical simulation methods. Researchers have started applying a wide range of machine learning models, in particular neural networks, to geophysical data for forecasting without these constraints. Constructing neural networks for forecasting such data is nontrivial, however, and often requires trial and error. To address these limitations, we focus on developing proper-orthogonal-decomposition-based long short-term memory networks (POD-LSTMs). We develop a scalable neural architecture search for generating stacked LSTMs to forecast temperature in the NOAA Optimum Interpolation Sea-Surface Temperature data set. Our approach identifies POD-LSTMs that are superior to manually designed variants and baseline time-series prediction methods. We also assess the scalability of different architecture search strategies on up to 512 Intel Knights Landing nodes of the Theta supercomputer at the Argonne Leadership Computing Facility.
