Real-time parameter updating for nonlinear digital twins using inverse mapping models and transient-based features

Abstract

In the context of digital twins, it is essential that a model gives an accurate description of the (controlled) dynamic behavior of a physical system during the system’s entire operational life. Therefore, model updating techniques are required that enable real-time updating of physically interpretable parameter values and are applicable to a wide range of (nonlinear) dynamical systems. As traditional, iterative, parameter updating methods may be computationally too expensive for real-time updating, the Inverse Mapping Parameter Updating (IMPU) method is proposed as an alternative. For this method, first, an Artificial Neural Network (ANN) is trained offline using novel features of simulated transient response data. Then, in the online phase, this ANN maps, with little computational cost, a set of measured output response features to parameter estimates enabling real-time model updating. In this paper, various types of transient response features are introduced to update parameter values of nonlinear dynamical systems with increased computational efficiency and accuracy. To analyze the efficacy of these features, the IMPU method is applied to a (simulated) nonlinear multibody system. It is shown that a smart selection of features, based on, e.g., the frequency content of the transient response, can improve the accuracy of the estimated parameter values, leading to more accurate updated models. Furthermore, the generalization capabilities of the ANNs are analyzed for these feature types, by varying the number of training samples and assessing the effect of incomplete training data. It is shown that the IMPU method can predict parameter values that are not part of the training data with acceptable accuracy as well.

Intelligent surveying and geographic information data updating method based on ArcGIS server

“…Proposed method Reference [2] Method Reference [3] 1, it can be seen that the proposed method performs well in terms of update delay. Compared to the methods in references [2] and [3] , the proposed method has a shorter update delay.…”

“…Proposed method Reference [2] Method Reference [3] 1, it can be seen that the proposed method performs well in terms of update delay. Compared to the methods in references [2] and [3] , the proposed method has a shorter update delay. Under different experimental frequencies, the update delay of the proposed method is lower than that of the other two methods, indicating that this method has high efficiency and stability.…”

“…In order to verify the overall effectiveness of the intelligent surveying and mapping geographic information data update method based on ArcGIS Server, a Shapefile format geographic information dataset was prepared as the experimental object, and ArcGIS Pro was used for data preprocessing operations, including coordinate conversion, format conversion, and data cleaning. The experimental tests were conducted using the methods of reference [2] and reference [3] as comparative methods.…”

“…According to the experimental results in Figure 1, it can be seen that the proposed method maintains an overall update consistency rate of over 80%, indicating that the method can effectively maintain the accuracy and completeness of the data. Compared to the methods in references [2] and [3] , the proposed method has a higher update consistency because it adopts a more intelligent and efficient data update strategy, which can better handle complex geographic information data changes. Based on the above analysis, it can be concluded that the proposed method has high accuracy and completeness in updating intelligent surveying and mapping geographic information data, and can meet the needs of practical applications.…”

“…Due to the complexity of DCHT, establishing and maintaining DCHT requires more computing and storage resources. Kessels et al [3] proposed the inverse mapping parameter update (IMPU) method as an alternative solution for real-time updates of dynamic systems. Firstly, offline training of artificial neural networks (ANNs) utilizes new features from simulated transient response data.…”