JpGU Meeting 2022
Session outline| Solid Earth Sciences (S) | ||
|---|---|---|
| Session Sub-category | Complex & General (CG) | |
| Session ID | S-CG51 | |
| Title | Driving Solid Earth Science through Machine Learning | |
| Short Title | Machine Learning in Solid Earth Sciences | |
| Main Convener | Name | Hisahiko Kubo |
| Affiliation | National Research Institute for Earth Science and Disaster Resilience | |
| Co-Convener 1 | Name | Yuki Kodera |
| Affiliation | Meteorological Research Institute, Japan Meteorological Agency | |
| Co-Convener 2 | Name | Makoto Naoi |
| Affiliation | Kyoto University | |
| Co-Convener 3 | Name | Keisuke Yano |
| Affiliation | The Institute of Statistical Mathematics | |
| Session Language | J | |
| Scope | Machine learning (ML) has brought innovations and remarkable results in various science fields including solid earth science. This session provides an opportunity to inspire each other for future developments by bringing studies of ML applications in various fields including solid earth science. We especially welcome studies that have led to the discovery of phenomena and theories in solid earth science. We also look forward to studies on the black box problem of AI and efforts toward explainable AI. | |
| Presentation Format | Oral and Poster session | |
| Joint with | ||
| Co-sponsored | - | |