bayesian estimation
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2022 ◽  
Vol 199 ◽  
pp. 105552
Alessio Sposato ◽  
Angela Fanelli ◽  
Marco Cordisco ◽  
Adriana Trotta ◽  
Michela Galgano ◽  

David Meenagh ◽  
Patrick Minford ◽  
Michael R. Wickens

AbstractPrice rigidity plays a central role in macroeconomic models but remains controversial. Those espousing it look to Bayesian estimated models in support, while those assuming price flexibility largely impose it on their models. So controversy continues unresolved by testing on the data. In a Monte Carlo experiment we ask how different estimation methods could help to resolve this controversy. We find Bayesian estimation creates a large potential estimation bias compared with standard estimation techniques. Indirect estimation where the bias is found to be low appears to do best, and offers the best way forward for settling the price rigidity controversy.

2022 ◽  
pp. 147592172110479
Sarah Miele ◽  
Pranav M Karve ◽  
Sankaran Mahadevan ◽  
Vivek Agarwal

This paper investigates the utility of physics-informed machine learning models for vibro-acoustic modulation (VAM)–based damage localization in concrete structures. Vibro-acoustic modulation is a nonlinear dynamics-based non-destructive testing method, which was initially developed to perform damage detection and later extended to accomplish damage localization. The VAM-based damage (hidden crack) diagnosis is performed by analyzing the damage index pattern on the surface of the component to arrive at the size and location of the hidden damage. Past investigations have employed heuristically selected damage index thresholds as well as computationally expensive Bayesian estimation methods for VAM-based damage localization in two (surface) dimensions. Compared to these studies, the proposed methodology automates the threshold selection (algorithmic instead of heuristic), increases the speed of the probabilistic damage diagnosis process, and enables the estimation of damage depth. We generate training data (damage index) for the machine learning models using the pertinent nonlinear dynamics (finite element) models using different combinations of test parameters. The (supervised) machine learning models are thus informed by computational physics models. These include two types of artificial neural network (ANN) models: classification models that identify whether a sensor location is damaged or not and regression models that enable Bayesian estimation to obtain the posterior probability distribution of damage location and size. The accuracy of machine learning-based diagnosis is evaluated using both numerical and laboratory experiments. The proposed physics-informed machine learning models for VAM-based damage diagnosis are able to achieve an accuracy of about 60–64% in the validation experiments, indicating the potential of these methods for internal crack detection. The results show that for complex (nonlinear dynamics-driven) diagnostic methods, damage index patterns learned from physics models could be successfully used for damage detection as well as localization.

2022 ◽  
Vol 15 (1) ◽  
pp. 19-28
Luz Judith R. Esparza ◽  
Fernando Baltazar-Larios

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