arXiv subjects

Ryosuke Yoneda

Publications and source records attributed to Ryosuke Yoneda.

7 recordsLinked to original sources

arXiv2025

Gaussian process regression with additive periodic kernels for two-body interaction analysis in coupled phase oscillators

Since many physical laws -- from classical mechanics to electromagnetism -- are formulated as two-body interactions, the same perspective naturally extends to biological and social dynamics. Here we focus on rhythmic phenomena, where phase reduction theory shows that synchronization dynamics can be universally described by coupled phase oscillators. Estimating the interaction functions of such systems from data offers a direct path to understanding and predicting such dynamics. Existing Fourier-series-based methods encounter difficulties with limited or biased data. To overcome this, we employ Gaussian process regression with additive periodic kernels. In our approach, we incorporate information about the estimation target into the statistical model in advance by designing kernel functions that capture the characteristics -- additivity and $2π$-periodicity -- of the coupling functions. Furthermore, owing to the Bayesian framework, our method enables the evaluation of uncertainty in the estimation results. We validate our approach on Van der Pol, FitzHugh-Nagumo, and spiking neural models. Our approach outperforms Fourier-series baselines in both error and stability under biased phase sampling. This enables data-driven studies of rhythm dynamics across a broader range of datasets. Furthermore, it makes a first step toward a statistically grounded, data-driven approach to general many-body systems with two-body interactions.

nlin.AO↗

arXiv2025

Treatment Effect Estimation for Graph-Structured Targets

Treatment effect estimation, which helps understand the causality between treatment and outcome variable, is a central task in decision-making across various domains. While most studies focus on treatment effect estimation on individual targets, in specific contexts, there is a necessity to comprehend the treatment effect on a group of targets, especially those that have relationships represented as a graph structure between them. In such cases, the focus of treatment assignment is prone to depend on a particular node of the graph, such as the one with the highest degree, thus resulting in an observational bias from a small part of the entire graph. Whereas a bias tends to be caused by the small part, straightforward extensions of previous studies cannot provide efficient bias mitigation owing to the use of the entire graph information. In this study, we propose Graph-target Treatment Effect Estimation (GraphTEE), a framework designed to estimate treatment effects specifically on graph-structured targets. GraphTEE aims to mitigate observational bias by focusing on confounding variable sets and consider a new regularization framework. Additionally, we provide a theoretical analysis on how GraphTEE performs better in terms of bias mitigation. Experiments on synthetic and semi-synthetic datasets demonstrate the effectiveness of our proposed method.

cs.LG↗

arXiv2023

Neural Network Approach to Scaling Analysis of Critical Phenomena

Determining the universality class of a system exhibiting critical phenomena is one of the central problems in physics. There are several methods to determine this universality class from data. As methods performing collapse plots onto scaling functions, polynomial regression, which is less accurate, and Gaussian process regression, which provides high accuracy and flexibility but is computationally heavy, have been proposed. In this paper, we propose a regression method using a neural network. The computational complexity is only linear in the number of data points. We demonstrate the proposed method for the finite-size scaling analysis of critical phenomena on the two-dimensional Ising model and bond percolation problem to confirm the performance. This method efficiently obtains the critical values with accuracy in both cases.

cond-mat.stat-mech↗

arXiv2021

The lower bound of the network connectivity guaranteeing in-phase synchronization

In-phase synchronization is a stable state of identical Kuramoto oscillators coupled on a network with identical positive connections, regardless of network topology. However, this fact does not mean that the networks always synchronize in-phase because other attractors besides the stable state may exist. The critical connectivity $μ_{\mathrm{c}}$ is defined as the network connectivity above which only the in-phase state is stable for all the networks. In other words, below $μ_{\mathrm{c}}$, one can find at least one network which has a stable state besides the in-phase sync. The best known evaluation of the value so far is $0.6828\cdots\leqμ_{\mathrm{c}}\leq0.75$. In this paper, focusing on the twisted states of the circulant networks, we provide a method to systematically analyze the linear stability of all possible twisted states on all possible circulant networks. This method using integer programming enables us to find the densest circulant network having a stable twisted state besides the in-phase sync, which breaks a record of the lower bound of the $μ_{\mathrm{c}}$ from $0.6828\cdots$ to $0.6838\cdots$. We confirm the validity of the theory by numerical simulations of the networks not converging to the in-phase state.

nlin.AO↗

arXiv2020

Critical exponents in coupled phase-oscillator models on small-world networks

A coupled phase-oscillator model consists of phase-oscillators, each of which has the natural frequency obeying a probability distribution and couples with other oscillators through a given periodic coupling function. This type of model is widely studied since it describes the synchronization transition, which emerges between the non-synchronized state and partially synchronized states. The synchronization transition is characterized by several critical exponents, and we focus on the critical exponent defined by coupling strength dependence of the order parameter for revealing universality classes. In a typical interaction represented by the perfect graph, an infinite number of universality classes is yielded by dependency on the natural frequency distribution and the coupling function. Since the synchronization transition is also observed in a model on a small-world network, whose number of links is proportional to the number of oscillators, a natural question is whether the infinite number of universality classes remains in small-world networks irrespective of the order of links. Our numerical results suggest that the number of universality class is reduced to one and the critical exponent is shared in the considered models having coupling functions up to the second harmonics with unimodal and symmetric natural frequency distributions.

nlin.AO↗

arXiv2019

Classification of bifurcation diagrams in coupled phase-oscillator models with asymmetric natural frequency distributions

Synchronization among rhythmic elements is modeled by coupled phase-oscillators each of which has the so-called natural frequency. A symmetric natural frequency distribution induces a continuous or discontinuous synchronization transition from the nonsynchronized state, for instance. It has been numerically reported that asymmetry in the natural frequency distribution brings new types of bifurcation diagram having, in the order parameter, oscillation or a discontinuous jump which emerges from a partially synchronized state. We propose a theoretical classification method of five types of bifurcation diagrams including the new ones, paying attention to the generality of the theory. The oscillation and the jump from partially synchronized states are discussed respectively by the linear analysis around the nonsynchronized state and by extending the amplitude equation up to the third leading term. The theoretical classification is examined by comparing with numerically obtained one.

nlin.CD↗

arXiv2018

A role of asymmetry in linear response of globally coupled oscillator systems

The linear response is studied in globally coupled oscillator systems including the Kuramoto model. We develop a linear response theory which can be applied to systems whose coupling functions are generic. Based on the theory, we examine the role of asymmetry introduced to the natural frequency distribution, the coupling function, or the coupling constants. A remarkable difference appears in coexistence of the divergence of susceptibility at the critical point and a nonzero phase gap between the order parameter and the applied external force. The coexistence is not allowed by the asymmetry in the natural frequency distribution but can be realized by the other two types of asymmetry. This theoretical prediction and the coupling-constant dependence of the susceptibility are numerically verified by performing simulations in $N$-body systems and in reduced systems obtained with the aid of the Ott-Antonsen ansatz.

nlin.AO↗