Julien Cotton

Deep learning for seismic velocity estimation and mapping from sparse data in underground environment

Valentin Tschannen, Kilyann Richard, Élodie Morgan, Tristan Barbagelata et autres

Characterizing rocks around underground engineered structures without drilling requires weakly intrusive sensing and methods able to exploit sparse, non-conventional seismic data. This constraint is particularly strong in High Level Waste (HLW) cells, where sensor placement is limited by design requirements and by …

us, fr (code pays fourni par la source)

EURAD-2 - Deliverable 15.5 - Digital Twins and their gaps in the Radioactive Waste Management domain

Arto Laikari, Diederik Jacques, Réka Szőke, Gabriela Roman Ross et autres

fi, ro, no, hu, fr, lt, ch, cz, es, de, it (code pays fourni par la source)

EURAD-2 - Deliverable 15.5 - Digital Twins and their gaps in the Radioactive Waste Management domain

Arto Laikari, Diederik Jacques, Réka Szőke, Gabriela Roman Ross et autres

fi, ro, no, hu, fr, lt, ch, cz, es, de, it (code pays fourni par la source)

Graph neural network structural limitation for thermal simulation and architecture optimization through rating system

Pierre Hembert, Chady Ghnatios, Julien Cotton, Francisco Chinesta

Abstract Graph neural networks are well suited for physics based simulation. Among other features, graphs can accurately represent thermal effects, with energy conservation operating on the nodes (vertices) and heat flow coursing through edges. Moreover, graph neural networks incorporate the data topology …

fr, us (code pays fourni par la source)

Leveraging advanced AI robotics for monitoring radioactive waste repositories: a review of current technologies, use cases and future trends

Laurent Ciarletta, Julien Cotton, Guillaume Hermand, Guy Pernot et autres

International audience

fr (code pays fourni par la source)

Hybrid Twins Modeling of a High-Level Radioactive Waste Cell Demonstrator for Long-Term Temperature Monitoring and Forecasting

D. Muñoz, Anoop Ebey Thomas, Julien Cotton, Johan Bertrand et autres

Monitoring a deep geological repository for radioactive waste during the operational phases relies on a combination of fit-for-purpose numerical simulations and online sensor measurements, both producing complementary massive data, which can then be compared to predict reliable and integrated information (e.g., in …

fr (code pays fourni par la source)

Assessing Sensor Integrity for Nuclear Waste Monitoring Using Graph Neural Networks

Pierre Hembert, Chady Ghnatios, Julien Cotton, Francisco Chinesta

A deep geological repository for radioactive waste, such as Andra's Cigéo project, requires long-term (persistent) monitoring. To achieve this goal, data from a network of sensors are acquired. This network is subject to deterioration over time due to environmental effects (radioactivity, mechanical …

fr (code pays fourni par la source)