Model Reduction for High-dimensional Nonlinear Chaotic Dynamical Systems Based on Kernel Multivariate Analysis
| Project/Area Number |
22740258
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
Mathematical physics/Fundamental condensed matter physics
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| Research Institution | Kagoshima University |
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Principal Investigator |
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| Project Period (FY) |
2010 – 2011
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| Project Status |
Completed (Fiscal Year 2011)
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| Budget Amount *help |
¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
Fiscal Year 2011: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
Fiscal Year 2010: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Keywords | 非平衡 / 非線形物理学 / 時系列解析 / カーネル法 / 多様体学習 / カオス / データ同化 / 水滴落下 / カーネル多変量解析 |
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| Research Abstract |
We improved ISOMAP, which is a representative manifold learning method, by preventing mistakes in estimating geodesics between points in a manifold using RANSAC. We applied the proposed approach to the numerical simulation data generated from a mass-spring model for dripping faucet in order to demonstrate the usefulness. Furthermore, we tried to analyze real data of the dripping faucet experiment and obtained promising results.
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Report
(3 results)
Research Products
(11 results)
