@misc{10481/90544,
year = {2024},
url = {https://hdl.handle.net/10481/90544},
abstract = {A new physics-guided Bayesian recurrent neural network is proposed in this manuscript. This hybrid algorithm
benefits from the knowledge in physics-based models, the capability of recurrent neural networks to handle
sequential data, and the flexibility of Bayesian methods to quantify the uncertainty. The introduction of physics
in the forward pass of the neural network significantly improves the results in multistep-ahead forecasting, and
the gradient-free nature of the Bayesian learning engine provides great flexibility to adapt to the observed data.
The proposed algorithm has been applied to a data-driven problem about fatigue in composites, and a case
study about accelerations in concrete buildings, where a comparison against the state-of-the-art algorithms
is also provided. The results revealed: (1) the accuracy of the proposals, comparable to the state-of-the-art
recurrent neural networks; (2) its stability during multiple runs of the algorithm, proving that it is a more
reliable option; (3) its precise quantification of the uncertainty, which provides useful information for the
subsequent decision-making process. As potential future applications to real world scenarios, the proposed
Bayesian recurrent neural network could be used in on-board PHM systems in the aerospace industry, or as
an on-site prediction tool in buildings for seismic events and/or aftershocks.},
organization = {ENHAnCE project, which has received
funding from the European Union’s Horizon 2020 research and innovation
programme under the Marie Skłodowska-Curie grant agreement
No 859957},
organization = {Funding for open access charge: Universidad de Granada
/ CBUA.},
publisher = {Elsevier},
keywords = {Physics-guided recurrent neural networks},
keywords = {Approximate Bayesian computation},
keywords = {Uncertainty quantification},
title = {Physics-guided recurrent neural network trained with approximate Bayesian computation: A case study on structural response prognostics},
doi = {10.1016/j.ress.2023.109822},
author = {Fernández Salas, Juan and Chiachío Ruano, Juan and Barros, José and Chiachío Ruano, Manuel and Kulkarni, Chetan S.},
}