arXiv subjects

Yuto Sasaki

Publications and source records attributed to Yuto Sasaki.

5 recordsLinked to original sources

arXiv2026

Constitutive flow law for hydrogel granular rafts near the brittle-ductile transition

Spatially varying flow laws have been identified in dry granular flow, yet their applicability to unjammed suspensions remains unclear. This study demonstrates that the quasistatic suspension flow combines dry granular rheology with nonlocal effects in the shear band and damped viscous flow in the outer creep region. Through rotary shear experiments on a hydrogel granular raft, we observe that the flow decays from the interface in the quasistatic regime, where the particles remain mobile even below the yield stress. These findings suggest the universal flow law across the transition between jammed/brittle granular behavior and unjammed/ductile viscous flow.

cond-mat.soft↗

arXiv2025

Origin of slow earthquake statistics in low-friction soft granular shear

Slow earthquakes differ from regular earthquakes in their slower moment release and size distribution dominated by smaller events. However, the physical origin of these slow earthquake statistics remains controversial. In this work, we experimentally demonstrate that their characteristics emerge from low-friction soft granular shear. To model slow-earthquake fault materials under hydrothermal conditions, we use a low-friction soft hydrogel particle layer floating on lubricating fluid and conduct stick-slip experiments. The observed slip events follow the same laws of both moment release rate and size distribution as with slow earthquakes, contrasting with frictional rigid granular shear. Slip size is determined by the competing effects of shear localization and pressure enhancement with decreasing porosity. These findings indicate that low friction and particle softness in sheared granular systems with sparse contact structures cause slow earthquake statistics, which may be driven by pore fluid dynamics and shear localization within hazardous fault zones.

physics.geo-ph↗

arXiv2025

Dislocation Glides in Monolayered Granular Media: Effect of Lattice Constant

A recent study demonstrated that granular crystals containing a single dislocation exhibit dislocation glide analogous to that observed in atomic-scale crystals, resulting in plastic deformation at yield stresses several orders of magnitude lower than those of dislocation-free crystals. The yielding behavior strongly depends on the interparticle friction coefficient $\mu$: dislocation glide occurs for friction coefficients below a critical value $\mu_c$, while crystalline order deteriorates above $\mu_c$. In this work, we use discrete element method simulations to systematically investigate how the lattice constant, which determines the interparticle spacing and is a fundamental parameter in microscopic crystalline solids, and the friction coefficient $\mu$ influence the yielding behavior in monolayered granular crystals with dislocation. By decreasing the lattice constant, we find an increase in the critical friction coefficient $\mu_c$, allowing dislocation glide to persist at higher friction values. Furthermore, we observe a linear scaling of yield stress with normal stress, except at extremely low friction coefficients.

cond-mat.soft↗

arXiv2025

Linearly Convergent Mixup Learning

Learning in the reproducing kernel Hilbert space (RKHS) such as the support vector machine has been recognized as a promising technique. It continues to be highly effective and competitive in numerous prediction tasks, particularly in settings where there is a shortage of training data or computational limitations exist. These methods are especially valued for their ability to work with small datasets and their interpretability. To address the issue of limited training data, mixup data augmentation, widely used in deep learning, has remained challenging to apply to learning in RKHS due to the generation of intermediate class labels. Although gradient descent methods handle these labels effectively, dual optimization approaches are typically not directly applicable. In this study, we present two novel algorithms that extend to a broader range of binary classification models. Unlike gradient-based approaches, our algorithms do not require hyperparameters like learning rates, simplifying their implementation and optimization. Both the number of iterations to converge and the computational cost per iteration scale linearly with respect to the dataset size. The numerical experiments demonstrate that our algorithms achieve faster convergence to the optimal solution compared to gradient descent approaches, and that mixup data augmentation consistently improves the predictive performance across various loss functions.

cs.LG↗

arXiv2024

Dislocation Glides in Granular Media

Atomic crystals with dislocations deform plastically at low stresses via dislocation glide. Whether dislocation glide occurs in macroscopic frictional granular media has remained unknown. The discrete element method is employed to simulate the structural and mechanical responses of a granular crystal with an edge dislocation. We find that dislocation glide occurs at low interparticle friction, resulting in significantly lower yield stresses than in dislocation-free crystals. Yield stress varies linearly with interparticle friction, attributed to both Peierls stress and frictional effect.

cond-mat.mtrl-sci↗