2021 Fiscal Year Final Research Report
Development of AI methodology with super-computed data for estimating earthquake disaster
| Project/Area Number |
18K18873
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| Research Category |
Grant-in-Aid for Challenging Research (Exploratory)
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| Allocation Type | Multi-year Fund |
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| Review Section |
Medium-sized Section 22:Civil engineering and related fields
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| Research Institution | The University of Tokyo |
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Principal Investigator |
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| Project Period (FY) |
2018-06-29 – 2022-03-31
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| Keywords | 人工知能 / 地震被害推定 / 大規模解析 |
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| Outline of Final Research Achievements |
In order to mitigate earthquake damage, many attempts have been made to improve the reliability of earthquake damage estimation. In this research, we developed an efficient artificial intelligence method for earthquake damage estimation by learning from large-scale numerical simulation results (supercomputed data) and physical considerations, and demonstrated its effectiveness through application examples. The reduction of the analysis cost is expected to contribute to fast and immediate earthquake damage estimation and to the plan for earthquake disaster prevention that takes into account many scenarios. We also show that artificial intelligence based on physical considerations can reduce the cost of analysis by shifting the analysis from conventional equation-based modeling to operations that are more suitable for recent computer architectures. The results show the potential of combining equation-based modeling and data-science methods.
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| Free Research Field |
地震工学，応用力学，計算科学
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| Academic Significance and Societal Importance of the Research Achievements |
このような解析コスト削減の実現は，高速な即時地震被害想定や，多数シナリオを考慮した事前震災対策用想定への貢献が期待される．また，物理的考察を踏まえた人工知能化により，従来のequation-based modelingによる解析を，昨今の高演算密度に適した計算機アーキテクチャに親和性の高い演算へシフトすることで解析コストの低減を実現できることを示す等，equation-based modelingとdata-science的な手法の融合の可能性を示す結果となっている．
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