论文标题
NINN：诱发诱导的神经网络
NINNs: Nudging Induced Neural Networks
论文作者
论文摘要
引入了称为nuding诱导的神经网络（NINN）的新算法，以控制和提高深神经网络（DNNS）的准确性。 NINNS框架可用于几乎所有先前存在的DNN，并具有向前传播，其成本与现有DNN相当。 NINNS通过在网络的正向传播中添加反馈控制术语来工作。反馈术语将神经网络推向所需的兴趣。与现有的数据同化算法（例如nuding）相比，NINN提供了多种优势，它们会导致更高的准确性。为NINN建立了严格的合并分析。算法和理论发现在数据同化和化学反应流中的示例中进行了说明。
New algorithms called nudging induced neural networks (NINNs), to control and improve the accuracy of deep neural networks (DNNs), are introduced. The NINNs framework can be applied to almost all pre-existing DNNs, with forward propagation, with costs comparable to existing DNNs. NINNs work by adding a feedback control term to the forward propagation of the network. The feedback term nudges the neural network towards a desired quantity of interest. NINNs offer multiple advantages, for instance, they lead to higher accuracy when compared with existing data assimilation algorithms such as nudging. Rigorous convergence analysis is established for NINNs. The algorithmic and theoretical findings are illustrated on examples from data assimilation and chemically reacting flows.
