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Egor I. Chetkin, Sergei L. Shishkin and Bogdan L. Kozyrskiy
Bayesian neural networks (BNNs) are effective tools for a variety of tasks that allow for the estimation of the uncertainty of the model. As BNNs use prior constraints on parameters, they are better regularized and less prone to overfitting, which is a s...
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Xudong Han, Yongling Fu, Yan Wang, Mingkang Wang and Deming Zhu
The control accuracy and stability of the electrohydrostatic actuator (EHA) are directly impacted by parameter uncertainty, disturbance uncertainty, and non-matching disturbance, which negatively impacts aircraft rudder maneuvering performance and even r...
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Alessia Di Giovanni, Diego Di Curzio, Davide Pantanella, Cristiana Picchi and Sergio Rusi
Recently, new numerical methods have been applied to weather data for the estimation of water budget, especially when the lack of measured data is considerable. Geostatistics is one of the most powerful approaches when it comes to studying spatially rele...
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Utkarsh Bhardwaj, Angelo Palos Teixeira and C. Guedes Soares
This paper assesses the uncertainty of the partial safety factors for the design of corroded pipelines against burst failure due to the variability associated with the strength model selection. First, 10 calibrated burst pressure prediction models for co...
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Anamika Dristi and Y. Jun Xu
Aquatic CO2 emission is typically estimated (i.e., not measured) through a gas exchange balance. Several factors can affect the estimation, primarily flow velocity and wind speed, which can influence a key parameter, the gas exchange coefficient KT in th...
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