Výsledky vyhledávání - "Naito, Hisashi"

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Crystal Structure Generation Based on Polyhedra using Dual Periodic Graphs

Autor: Yokoyama, Tomoyasu, Ichikawa, Kazuhide, Naito, Hisashi

Crystal structure design is important for the discovery of new highly functional materials because crystal structure strongly influences material properties. Crystal structures are composed of space-filling polyhedra, which affect material properties

Externí odkaz: http://arxiv.org/abs/2312.09060

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Stable configurations of entangled graphs and weaves with repulsive interactions

Autor: Kotani, Motoko, Naito, Hisashi, Shinkawa, Eriko

Entangled objects such as entangled graphs and weaves are often seen in nature. In the present article, two identical graphs entangled, and weaves with two different color threads are studied. A method to identify stable configurations in the three d

Externí odkaz: http://arxiv.org/abs/2309.01613

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A Weierstrass Representation Formula for Discrete Harmonic Surfaces

Autor: Kotani, Motoko, Naito, Hisashi

Publikováno v: SIGMA 20 (2024), 034, 15 pages

A discrete harmonic surface is a trivalent graph which satisfies the balancing condition in the 3-dimensional Euclidean space and achieves energy minimizing under local deformations. Given a topological trivalent graph, a holomorphic function, and an

Externí odkaz: http://arxiv.org/abs/2307.08537

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Neural Polytopes

Autor: Hashimoto, Koji, Naito, Tomoya, Naito, Hisashi

We find that simple neural networks with ReLU activation generate polytopes as an approximation of a unit sphere in various dimensions. The species of polytopes are regulated by the network architecture, such as the number of units and layers. For a

Externí odkaz: http://arxiv.org/abs/2307.00721

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Multi-body wave function of ground and low-lying excited states using unornamented deep neural networks

Autor: Naito, Tomoya, Naito, Hisashi, Hashimoto, Koji

Publikováno v: Phys. Rev. Research 5, 033189 (2023)

We propose a method to calculate wave functions and energies not only of the ground state but also of low-lying excited states using a deep neural network and the unsupervised machine learning technique. For systems composed of identical particles, a

Externí odkaz: http://arxiv.org/abs/2302.08965

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