日本物理学会, 第29回(2024年)論文賞,Detection of Phase Transition via Convolutional Neural NetworksAkinori Tanaka Akio Tomiya
2019年6月
2019年度素粒子論委員会, 素粒子メダル奨励賞,Detection of Phase Transition via Convolutional Neural Networks富谷昭夫 田中章詞
2019年4月
Journal of the Physical Society of Japan, Most Cited Articles in 2018 from Vol. 86 (2017),Detection of Phase Transition via Convolutional Neural Networks富谷昭夫 田中章詞
We study the axial U(1) symmetry at a finite temperature in two-flavor lattice QCD. Employing the Mobius domain-wall fermions, we generate gauge configurations slightly above the critical temperature Tc with different lattice sizes L = 2-4 fm. Our...
We provide an origin of family replications in the standard model of particle<br />
physics by constructing renormalizable, asymptotically free, four dimensional<br />
local gauge theories that dynamically generate the fifth and sixth dimensions<b...
Pawel Caputa   Sumit R. Das   Masahiro Nozaki   Akio Tomiya   
2017年2月
Global quantum quench with a finite quench rate which crosses critical points<br />
is known to lead to universal scaling of correlation functions as functions of<br />
the quench rate. In this work, we explore scaling properties of the<br />
enta...
We design a Convolutional Neural Network (CNN) which studies correlation<br />
between discretized inverse temperature and spin configuration of 2D Ising<br />
model and show that it can find a feature of the phase transition without<br />
teachin...
We argue that the axionic domain-wall with a QCD bias may be incompatible
with the NANOGrav 15-year data on a stochastic gravitational wave (GW)
background, when the domain wall network collapses in the hot-QCD induced local
CP-odd domain. This is...
We present our sparse modeling study to extract spectral functions from
Euclidean-time correlation functions. In this study covariance between
different Euclidean times of the correlation function is taken into account,
which was not done in previ...
Machine learning, deep learning, has been accelerating computational physics,
which has been used to simulate systems on a lattice. Equivariance is essential
to simulate a physical system because it imposes a strong induction bias for
the probabil...
Machine learning and deep learning have revolutionized computational physics,
particularly the simulation of complex systems. Equivariance is essential for
simulating physical systems because it imposes a strong inductive bias on the
probability d...
Peter Boyle   Taku Izubuchi   Luchang Jin   Chulwoo Jung   Christoph Lehner   Nobuyuki Matsumoto   Akio Tomiya   
2022年12月
We construct an approximate trivializing map by using a Schwinger-Dyson
equation. The advantage of this method is that: (1) The basis for the flow
kernel can be chosen arbitrarily by hand. (2) It can be applied to the general
action of interest. (...