| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2019: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2018: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2017: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
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| Outline of Final Research Achievements |
We have reduced the computational cost of fluid dynamics by using various data-driven methods in machine learning. Numerical calculation of turbulence requires an enormous calculation cost, but it became significantly small by solving the model of the minimum necessary variables constructed by deep learning. Using dynamic mode decomposition, which is one of data-driven algorism, we devised a method that enables efficient control of object motion in a fluid by using modes decomposed from flow data. The reinforcement learning algorithm was modified to be suitable for flow control, enabling low-cost reinforcement learning.
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