37 citations · 56 across the 6 of their papers we have counts for
6 papers
Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics
Gert Aarts, Kenji Fukushima, Tetsuo Hatsuda +4
The integration of deep learning techniques and physics-driven designs is reforming the way we address inverse problems, in which accurate physical properties are extracted from co…
Stochastic quantization and diffusion models
Kenji Fukushima, Syo Kamata
This is a pedagogical review of the possible connection between the stochastic quantization in physics and the diffusion models in machine learning. For machine-learning applicatio…
Speed of sound and trace anomaly in a unified treatment of the two-color diquark superfluid, the pion-condensed high-isospin matter, and the 2SC quark matter
Kenji Fukushima, Shuhei Minato
In a unified perturbative treatment from the high-density side, we compute the speed of sound and the trace anomaly as functions of the chemical potential for the two-color diq…
Preponderant Orbital Polarization in Relativistic Magnetovortical Matter
Kenji Fukushima, Koichi Hattori, Kazuya Mameda
We establish thermodynamic stability and gauge invariance in the magnetovortical matter of Dirac fermions under the coexistent rotation and strong magnetic field. The corresponding…
Signature of hadron-quark crossover in binary-neutron-star mergers
Yuki Fujimoto, Kenji Fukushima, Kenta Hotokezaka +1
We study observational signatures of the hadron-quark crossover in binary-neutron-star mergers by numerical-relativity simulations with various mass configurations. We employ two e…
Uncertainty quantification in the machine-learning inference from neutron star probability distribution to the equation of state
Yuki Fujimoto, Kenji Fukushima, Syo Kamata +1
We discuss the machine-learning inference and uncertainty quantification for the equation of state (EoS) of the neutron star (NS) matter directly using the NS probability distribut…