| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2021: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2020: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2019: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2018: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
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| Outline of Final Research Achievements |
The purpose of this project is developing Markov Chain Monte-Carlo (MCMC) method accelerated by machine learning techniques. To achieve it, I focus on so-called Self Learning Monte-Carlo (SLMC) method that enables training during execution of MCMC and modifying the bias of the trained model in the generation step, and I apply this method to some physical theories. In the end of this project, we have achieved to make SLMC supported simulation of lattice gauge theory with dynamical fermions and non-commutative gauge group (SU(2)). We have checked its results and shown reduction of the autocorrelation.
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