arXiv subjects

Takanori Ishii

Publications and source records attributed to Takanori Ishii.

4 recordsLinked to original sources

arXiv2026

Black-hole formation and thermalization in open JT gravity

Black-hole formation is expected, via holography, to correspond to thermalization in the boundary theory. For open quantum systems, an initial pure state generically evolves into a mixed state irreversibly, suggesting that horizon formation in the bulk should arise. In this paper, we extend the holographic Lindblad prescription to a non-Markovian setting and apply it to JT gravity coupled to a scalar field. Using numerical simulations in the semiclassical and high-temperature regime, we demonstrate the dynamical formation of black holes.

hep-th↗

arXiv2026

Symmetry-aware Conditional Generation of Crystal Structures Using Diffusion Models

The application of generative models in crystal structure prediction (CSP) has gained significant attention. Conditional generation--particularly the generation of crystal structures with specified stability or other physical properties has been actively researched for material discovery purposes. Meanwhile, the generative models capable of symmetry-aware generation are also under active development, because space group symmetry has a strong relationship with the physical properties of materials. In this study, we demonstrate that the symmetry control in the previous conditional crystal generation model may not be sufficiently effective when space group constraints are applied as a condition. To address this problem, we propose the WyckoffDiff-Adaptor, which embeds conditional generation within a WyckoffDiff architecture that effectively diffuses Wyckoff positions to achieve precise symmetry control. We successfully generated formation energy phase diagrams while specifying stable structures of particular combination of elements, such as Li--O and Ti--O systems, while simultaneously preserving the symmetry of the input conditions. The proposed method with symmetry-aware conditional generation demonstrates promising results as an effective approach to achieving the discovery of novel materials with targeted physical properties.

cond-mat.mtrl-sci↗

arXiv2025

Lindblad dynamics in holography

We develop, in the AdS/CFT correspondence, a method to compute correlation functions when the CFT is governed by the Lindblad equation for open quantum systems, via the AdS theory. Using a simple example in AdS$_3$/CFT$_2$, we demonstrate that the predictions of the AdS theory based on our method match the direct computations in the dual CFT. We also briefly discuss the relaxation problem and the holographic entropy in this example.

hep-th↗

arXiv2025

Data-driven discovery of self-similarity using neural networks

Finding self-similarity is a key step for understanding the governing law behind complex physical phenomena. Traditional methods for identifying self-similarity often rely on specific models, which can introduce significant bias. In this paper, we present a novel neural network-based approach that discovers self-similarity directly from observed data, without presupposing any models. The presence of self-similar solutions in a physical problem signals that the governing law contains a function whose arguments are given by power-law monomials of physical parameters, which are characterized by power-law exponents. The basic idea is to enforce such particular forms structurally in a neural network in a parametrized way. We train the neural network model using the observed data, and when the training is successful, we can extract the power exponents that characterize scale-transformation symmetries of the physical problem. We demonstrate the effectiveness of our method with both synthetic and experimental data, validating its potential as a robust, model-independent tool for exploring self-similarity in complex systems.

cond-mat.soft↗