Model-assisted deep learning of rare extreme events from partial observations
Model-assisted deep learning of rare extreme events from partial observations
复制标题
基于部分观测的罕见极端事件的模型辅助深度学习
DOI：
10.1063/5.0077646
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发表时间：
2021-11
期刊：
影响因子：
2.9
通讯作者：
Anna Asch;Ethan Brady;Hugo Gallardo;John Hood;Bryan Chu;M. Farazmand
中科院分区：
文献类型：
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作者：
Anna Asch;Ethan Brady;Hugo Gallardo;John Hood;Bryan Chu;M. Farazmand
为了利用深度神经网络预测罕见的极端事件，人们会遇到所谓的小数据问题，因为即便长期观测，往往也只能获得少量极端事件样本。在此，我们探究一种模型辅助框架，其中训练数据并非来自观测，而是源于数值模拟，这些模拟能提供足够的极端事件样本。然而，为确保训练出的网络在实际中可用，训练并非基于完整的模拟数据进行；相反，我们仅使用一小部分可在实际中测量的可观测量。我们在三个不同的动力系统（罗斯勒吸引子、菲茨休 - 纳古莫模型以及湍流流体流动）和三种不同的深度神经网络架构（前馈神经网络、长短期记忆网络和回声状态网络）上研究这种模型辅助框架的可行性。在每种情况下，我们都会研究预测精度、对噪声的鲁棒性、重复训练下的可重复性以及对输入数据类型的敏感性。特别地，我们发现长短期记忆网络对噪声的鲁棒性最强，能给出相对准确的预测，同时对超参数的微调要求最低。
To predict rare extreme events using deep neural networks, one encounters the so-called small data problem because even long-term observations often contain few extreme events. Here, we investigate a model-assisted framework where the training data are obtained from numerical simulations, as opposed to observations, with adequate samples from extreme events. However, to ensure the trained networks are applicable in practice, the training is not performed on the full simulation data; instead, we only use a small subset of observable quantities, which can be measured in practice. We investigate the feasibility of this model-assisted framework on three different dynamical systems (Rössler attractor, FitzHugh-Nagumo model, and a turbulent fluid flow) and three different deep neural network architectures (feedforward, long short-term memory, and reservoir computing). In each case, we study the prediction accuracy, robustness to noise, reproducibility under repeated training, and sensitivity to the type of input data. In particular, we find long short-term memory networks to be most robust to noise and to yield relatively accurate predictions, while requiring minimal fine-tuning of the hyperparameters.