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
Heng-Tong Ding Olaf Kaczmarek Frithjof Karsch Sheng-Tai Li Swagato Mukherjee Akio Tomiya Yu Zhang
2020年1月
We utilize the eigenvalue filtering technique combined with the stochastic
estimate of the mode number to determine the eigenvalue spectrum. Simulations
of (2 + 1)-flavor QCD are performed using the Highly Improved Staggered Quarks
(HISQ/tree) act...
Bipasha Chakraborty Masazumi Honda Taku Izubuchi Yuta Kikuchi Akio Tomiya
Physical Review D 2020年1月
We perform a digital quantum simulation of a gauge theory with a topological
term in Minkowski spacetime, which is practically inaccessible by standard
lattice Monte Carlo simulations. We focus on dimensional quantum
electrodynamics with the...
Keun-Young Kim Mitsuhiro Nishida Masahiro Nozaki Minsik Seo Yuji Sugimoto Akio Tomiya
2019年6月
We study the time evolution of the entanglement entropy after quantum
quenches in Lifshitz free scalar theories, with the dynamical exponent ,
by using the correlator method. For quantum quenches we consider two types of
time-dependent mass f...
Akio Tomiya Heng-Tong Ding Xiao-Dan Wang Yu Zhang Swagato Mukherjee Christian Schmidt
2019年4月
We study the phase structure of QCD with three degenerate flavors in the<br />
external magnetic fields using highly improved staggered quarks (HISQ). The<br />
simulations are performed on lattice. In order to investigate<br />
the...
We discuss an aspect of neural networks for the purpose of phase transition<br />
detection. To this end, we first train the neural network by feeding<br />
Ising/Potts configurations with labels of temperature so that it can predict<br />
the tem...
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. (...
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