自编码
计算机科学
数据同化
卡尔曼滤波器
状态空间表示
动态贝叶斯网络
推论
动态数据
人工智能
代表(政治)
状态空间
人工神经网络
算法
机器学习
贝叶斯概率
数学
法学
程序设计语言
统计
气象学
物理
政治
政治学
作者
Maddalena Amendola,Rossella Arcucci,Laetitia Mottet,César Quilodrán Casas,Shiwei Fan,Christopher C. Pain,P. F. Linden,Yike Guo
标识
DOI:10.1007/978-3-030-77977-1_30
摘要
Data Assimilation (DA) is a Bayesian inference that combines the state of a dynamical system with real data collected by instruments at a given time. The goal of DA is to improve the accuracy of the dynamic system making its result as real as possible. One of the most popular technique for DA is the Kalman Filter (KF). When the dynamic system refers to a real world application, the representation of the state of a physical system usually leads to a big data problem. For these problems, KF results computationally too expensive and mandates to use of reduced order modeling techniques. In this paper we proposed a new methodology we called Latent Assimilation (LA). It consists in performing the KF in the latent space obtained by an Autoencoder with non-linear encoder functions and non-linear decoder functions. In the latent space, the dynamic system is represented by a surrogate model built by a Recurrent Neural Network. In particular, an Long Short Term Memory (LSTM) network is used to train a function which emulates the dynamic system in the latent space. The data from the dynamic model and the real data coming from the instruments are both processed through the Autoencoder. We apply the methodology to a real test case and we show that the LA has a good performance both in accuracy and in efficiency.
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