论文标题
神经网络潜在空间中的数据同化
Data Assimilation in the Latent Space of a Neural Network
论文作者
论文摘要
迫切需要建立模型来解决室内空气质量问题。由于该模型应该是准确且快速的，因此减少订单建模技术用于降低问题的维度。代表动态系统的模型的准确性正在改善使用数据同化技术来自传感器的真实数据。在本文中，我们制定了一种称为潜在同化的新方法，该方法结合了数据同化和机器学习。我们使用卷积神经网络来降低问题的维度，这是一个长期记忆，以构建动态系统的替代模型和最佳的插值Kalman滤波器以结合实际数据。为室内空间内的二氧化碳浓度提供了实验结果。该方法可用于例如实时预测空气中病毒的负载，例如SARS-COV-2，通过将其与CO2的浓度联系起来。
There is an urgent need to build models to tackle Indoor Air Quality issue. Since the model should be accurate and fast, Reduced Order Modelling technique is used to reduce the dimensionality of the problem. The accuracy of the model, that represent a dynamic system, is improved integrating real data coming from sensors using Data Assimilation techniques. In this paper, we formulate a new methodology called Latent Assimilation that combines Data Assimilation and Machine Learning. We use a Convolutional neural network to reduce the dimensionality of the problem, a Long-Short-Term-Memory to build a surrogate model of the dynamic system and an Optimal Interpolated Kalman Filter to incorporate real data. Experimental results are provided for CO2 concentration within an indoor space. This methodology can be used for example to predict in real-time the load of virus, such as the SARS-COV-2, in the air by linking it to the concentration of CO2.
