
セッション概要
| 領域外・複数領域 (M) | ||||
|---|---|---|---|---|
| セッション小記号 | 計測技術・研究手法 (TT) | |||
| セッション ID | M-TT45 | |||
| タイトル | Artificial Intelligence in Earth and Environmental sciences | |||
| タイトル短縮名 | AI in Earth and Environmental sciences | |||
| 代表コンビーナ | 氏名 | Dmitri A Kondrashov | ||
| 所属 | University of California Los Angeles | |||
| 共同コンビーナ 1 | 氏名 | Mikhail Krinitskiy | ||
| 所属 | Shirshov Institute of Oceanology, Russian Academy of Sciences | |||
| 共同コンビーナ 2 | 氏名 | Ingo Richter | ||
| 所属 | JAMSTEC Japan Agency for Marine-Earth Science and Technology | |||
| 共同コンビーナ 3 | 氏名 | 東塚 知己 | ||
| 所属 | 東京大学大学院理学系研究科地球惑星科学専攻 | |||
| セッション言語 | E | |||
| スコープ | Cutting-edge developments in artificial intelligence (AI) are revolutionizing Earth and environmental sciences. This session provides a forum to explore and advance AI-driven innovations that leverage data to deepen insights into our planet's history, current state, and future trajectories. We welcome research on sophisticated AI approaches -- such as machine learning (ML), neural networks, and deep learning -- applied to diverse fields like atmospheric science, oceanography, climate studies, geospace, and other geophysical domains. Topics of interest include, but are not limited to, techniques for analyzing large datasets (e.g., pattern recognition, inverse problems); data-driven modeling and forecasting (e.g., dimensionality reduction, inverse modeling); methods integrating data with physical models (e.g., physics-informed ML, data assimilation); and advanced mathematical, statistical, or theory-driven ML approaches (e.g., optimization, causal inference, Koopman and Mori-Zwanzig frameworks). Join us for engaging presentations and discussions in this dynamic, interdisciplinary domain. |
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| セッション形式 | 口頭およびポスターセッション | |||