NP6.2

Turbulence, reconnection and shocks are fundamental non-linear processes observed in solar, heliospheric, magnetospheric and laboratory plasmas. These processes are not separate, but rather appear to be interconnected. For instance, a close link exists between reconnection and turbulence. On the one hand the turbulence cascade favors the onset of magnetic reconnection between magnetic islands and, on the other hand, magnetic reconnection is able to trigger turbulence in the reconnection outflows and separatrices. Similarly, shocks may form in collisional and collisionless reconnection processes and can be responsible for turbulence formation, as for instance in the turbulent magnetosheath.

This session welcomes simulations, observational and theoretical works relevant for the study of these non-linear phenomena. Particularly welcome will be works focusing on the link between them in a range of scale going from fluid MHD to kinetic. This year we encourage especially papers proposing new methods, especially those rooted in Artificial Intelligence (AI) and Machine Learning (ML), to extract new knowledge from big observational and simulated data sets.

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Co-organized by ST1
Convener: Maria Elena Innocenti | Co-conveners: Francesco Pucci, Meng Zhou, Giovanni Lapenta, Luca Sorriso-Valvo
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| Attendance Wed, 06 May, 16:15–18:00 (CEST)

Turbulence, reconnection and shocks are fundamental non-linear processes observed in solar, heliospheric, magnetospheric and laboratory plasmas. These processes are not separate, but rather appear to be interconnected. For instance, a close link exists between reconnection and turbulence. On the one hand the turbulence cascade favors the onset of magnetic reconnection between magnetic islands and, on the other hand, magnetic reconnection is able to trigger turbulence in the reconnection outflows and separatrices. Similarly, shocks may form in collisional and collisionless reconnection processes and can be responsible for turbulence formation, as for instance in the turbulent magnetosheath.

This session welcomes simulations, observational and theoretical works relevant for the study of these non-linear phenomena. Particularly welcome will be works focusing on the link between them in a range of scale going from fluid MHD to kinetic. This year we encourage especially papers proposing new methods, especially those rooted in Artificial Intelligence (AI) and Machine Learning (ML), to extract new knowledge from big observational and simulated data sets.

Public information: please check EGU chat for new zoom link and password

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