Speaker
Shunsuke Yasunaga
(Institute of Science Tokyo)
Description
Domain-wall fermions provide a good lattice realization of chiral fermions by introducing an additional fifth dimension. Achieving improved chiral symmetry typically necessitates increasing the extent of this dimension at the expense of significantly higher computational cost. We propose a machine-learning-based parameter-optimization approach that emulates the effect of a longer fifth dimension while keeping it short, thereby reducing the computational cost.
| Parallel Session (for talks only) | Algorithms and artificial intelligence |
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Author
Shunsuke Yasunaga
(Institute of Science Tokyo)
Co-authors
Akio Tomiya
(Tokyo Woman's Christian University)
Kenta Yoshimura
(Institute of Science Tokyo)
Yuki Nagai
(The University of Tokyo)
