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Bayesian system identification of dynamical systems using highly informative training data

机译：使用信息量高的训练数据对动力系统进行贝叶斯系统识别

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摘要

This paper is concerned with the Bayesian system identification of structural dynamical systems using experimentally obtained training data. It is motivated by situations where, from a large quantity of training data, one must select a subset to infer probabilistic models. To that end, using concepts from information theory, expressions are derived which allow one to approximate the effect that a set of training data will have on parameter uncertainty as well as the plausibility of candidate model structures. The usefulness of this concept is then demonstrated through the system identification of several dynamical systems using both physics-based and emulator models. The result is a rigorous scientific framework which can be used to select 'highly informative' subsets from large quantities of training data.
机译：本文涉及利用实验获得的训练数据对结构动力系统进行贝叶斯系统辨识。它是由以下情况激发的：必须从大量训练数据中选择一个子集以推断概率模型。为此，使用信息论中的概念，导出了表达式，使表达式可以近似地估算出一组训练数据对参数不确定性以及候选模型结构的合理性的影响。然后，通过使用基于物理学的模型和仿真器模型对几个动力学系统进行系统识别，证明了此概念的实用性。结果是建立了严格的科学框架，可用于从大量训练数据中选择“高度信息化”的子集。

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