Academic Journal
Physics-informed deep learning approach for modeling crustal deformation
| العنوان: | Physics-informed deep learning approach for modeling crustal deformation |
|---|---|
| المؤلفون: | Tomohisa Okazaki, Takeo Ito, Kazuro Hirahara, Naonori Ueda |
| المصدر: | Nature Communications, Vol 13, Iss 1, Pp 1-9 (2022) |
| بيانات النشر: | Nature Portfolio, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Science |
| مصطلحات موضوعية: | Science |
| الوصف: | Modeling crustal deformation is critical for understanding of tectonic processes and earthquake potentials. Here, the authors propose a deep learning approach that can be extended in a straightforward manner to complex crustal structures and inverse problems. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2041-1723 |
| Relation: | https://doaj.org/toc/2041-1723 |
| DOI: | 10.1038/s41467-022-34922-1 |
| URL الوصول: | https://doaj.org/article/a2c4df6a89bd4d758cb3e695afcf45d1 |
| رقم الانضمام: | edsdoj.2c4df6a89bd4d758cb3e695afcf45d1 |
| قاعدة البيانات: | Directory of Open Access Journals |
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