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
Koji Hashimoto Sotaro Sugishita Akinori Tanaka Akio Tomiya
Phys. Rev. D 98, 106014 (2018) 2018年9月
We apply the relation between deep learning (DL) and the AdS/CFT<br />
correspondence to a holographic model of QCD. Using a lattice QCD data of the<br />
chiral condensate at a finite temperature as our training data, the deep<br />
learning proc...
Koji Hashimoto Sotaro Sugishita Akinori Tanaka Akio Tomiya
Phys. Rev. D 98, 046019 (2018) 2018年2月
We present a deep neural network representation of the AdS/CFT<br />
correspondence, and demonstrate the emergence of the bulk metric function via<br />
the learning process for given data sets of response in boundary quantum field<br />
theories....
We study dynamics of quantum entanglement in smooth global quenches with a<br />
finite rate, by computing the time evolution of entanglement entropy in 1 + 1<br />
dimensional free scalar theory with time-dependent masses which start from a<br />...
In this paper we propose new algorithm to reduce autocorrelation in Markov<br />
chain Monte-Carlo algorithms for euclidean field theories on the lattice. Our<br />
proposing algorithm is the Hybrid Monte-Carlo algorithm (HMC) with restricted<br /...
Akio Tomiya Heng-Tong Ding Swagato Mukherjee Christian Schmidt Xiao-Dan Wang
2017年11月
Lattice simulations for (2+1)-flavor QCD with external magnetic field<br />
demonstrated that the quark mass is one of the important parameters responsible<br />
for the (inverse) magnetic catalysis. We discuss the dependences of chiral<br />
cond...
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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