D Faranda1, G Messori2, T Alberti3

  • 1<a href="https://ror.org/03dsd0g48">Laboratoire des Sciences du Climat et de l'Environnement</a>, UMR 8212 CEA-CNRS-UVSQ, <a href="https://ror.org/03xjwb503">Université Paris-Saclay</a> and IPSL, <a href="https://ror.org/03n15ch10">CEA Saclay</a> l'Orme des Merisiers, 91191 Gif-sur-Yvette, France; <a href="https://ror.org/03cznfh76">London Mathematical Laboratory</a>, 8 Margravine Gardens, London W6 8RH, United Kingdom; and <a href="https://ror.org/000ehr937">Laboratoire de Météorologie Dynamique/IPSL</a>, École Normale Supérieure, PSL Research University, <a href="https://ror.org/02en5vm52">Sorbonne Université</a>, École Polytechnique, IP Paris, CNRS, 75005 Paris, France.

Physical review. E
|November 20, 2024
PubMed
概括

统计物理学和动态系统理论为极端地质物理事件提供了洞察力. 这项研究强调了需要随机方法来更好地理解这些现象背后的物理.

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