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Constrained Recurrent Bayesian Forecasting for Crack Propagation

Autor: Ouerk, Sara Yasmine, Van, Olivier Vo, Yagoubi, Mouadh

Predictive maintenance of railway infrastructure, especially railroads, is essential to ensure safety. However, accurate prediction of crack evolution represents a major challenge due to the complex interactions between intrinsic and external factors

Externí odkaz: http://arxiv.org/abs/2410.14761

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Hybrid data driven/thermal simulation model for comfort assessment

Autor: Barbedienne, Romain, Ouerk, Sara Yasmine, Yagoubi, Mouadh, Bouia, Hassan, Kaemmerlen, Aurelie, Charrier, Benoit

Machine learning models improve the speed and quality of physical models. However, they require a large amount of data, which is often difficult and costly to acquire. Predicting thermal comfort, for example, requires a controlled environment, with p

Externí odkaz: http://arxiv.org/abs/2309.01734

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Rail Crack Propagation Forecasting Using Multi-horizons RNNs

Autor: Ouerk, Sara Yasmine, Van, Olivier Vo, Yagoubi, Mouadh

The prediction of rail crack length propagation plays a crucial role in the maintenance and safety assessment of materials and structures. Traditional methods rely on physical models and empirical equations such as Paris law, which often have limitat

Externí odkaz: http://arxiv.org/abs/2309.01569

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