First encounters on combs

First-passage properties of bundled networks

Bundled networks, obtained by attaching a copy of a fiber structure to each node on the base structure, serve as important realistic models for the geometry and dynamics of nontranslationally invariant systems in condensed matter physics. Here, we analyze the first-passage properties, including the mean first-passage time, the mean-trapping time, the global-mean first-passage time (GFPT), and the stationary distribution, of a biased random walk within such networks, in which a random walker moves to a neighbor on base with probability γ and to a neighbor on fiber with probability 1-γ when the walker at a node on base. We reveal the primary properties of both the base and fiber structure, which govern the first-passage characteristics of the bundled network. Explicit expressions between these quantities in the bundled networks and the related quantities in the component structures are presented. GFPT serves as a crucial indicator for evaluating network transport efficiency. Unexpectedly, bases and fibers with similar scaling of GFPT can construct bundled networks exhibiting different scaling behaviors of GFPT. Therefore, bundled networks can be tailored to accommodate specific dynamic property requirements by choosing a suitable base and fiber structure. These findings contribute to advancing the design and optimization of network structures.

First encounters on Watts-Strogatz networks and Barabási-Albert networks

The Watts-Strogatz networks are important models that interpolate between regular lattices and random graphs, and Barabási-Albert networks are famous models that explain the origin of the scale-free networks. Here, we consider the first encounters between two particles (e.g., prey A and predator B) embedded in the Watts-Strogatz networks and the Barabási-Albert networks. We address numerically the mean first-encounter time (MFET) while the two particles are moving and the mean first-passage time (MFPT) while the prey is fixed, aiming at uncovering the impact of the prey's motion on the encounter time, and the conditions where the motion of the prey would accelerate (or slow) the encounter between the two particles. Different initial conditions are considered. In the case where the two particles start independently from sites that are selected randomly from the stationary distribution, on the Barabási-Albert networks, the MFET is far less than the MFPT, and the impact of prey's motion on the encounter time is enormous, whereas, on the Watts-Strogatz networks (including Erdős-Rényi random networks), the MFET is about 0.5-1 times the MFPT, and the impact of prey's motion on the encounter time is relatively small. We also consider the case where prey A starts from a fixed site and the predator starts from a randomly drawn site and present the conditions where the motion of the prey would accelerate (or slow) the encounter between the two particles. The relation between the MFET (or MFPT) and the average path length is also discussed.

First-encounter time of two diffusing particles in two- and three-dimensional confinement

The statistics of the first-encounter time of diffusing particles changes drastically when they are placed under confinement. In the present work, we make use of Monte Carlo simulations to study the behavior of a two-particle system in two- and three-dimensional domains with reflecting boundaries. Based on the outcome of the simulations, we give a comprehensive overview of the behavior of the survival probability S(t) and the associated first-encounter time probability density H(t) over a broad time range spanning several decades. In addition, we provide numerical estimates and empirical formulas for the mean first-encounter time 〈T〉, as well as for the decay time T characterizing the monoexponential long-time decay of the survival probability. Based on the distance between the boundary and the center of mass of two particles, we obtain an empirical lower bound t_{B} for the time at which S(t) starts to significantly deviate from its counterpart for the no boundary case. Surprisingly, for small-sized particles, the dominant contribution to T depends only on the total diffusivity D=D_{1}+D_{2}, in sharp contrast to the one-dimensional case. This contribution can be related to the Wiener sausage generated by a fictitious Brownian particle with diffusivity D. In two dimensions, the first subleading contribution to T is found to depend weakly on the ratio D_{1}/D_{2}. We also investigate the slow-diffusion limit when D_{2}≪D_{1}, and we discuss the transition to the limit when one particle is a fixed target. Finally, we give some indications to anticipate when T can be expected to be a good approximation for 〈T〉.

Run with the Brownian Hare, Hunt with the Deterministic Hounds

We present analytic results for mean capture time and energy expended by a pack of deterministic hounds actively chasing a randomly diffusing prey. Depending on the number of chasers, the mean capture time as a function of the prey's diffusion coefficient can be monotonically increasing, decreasing, or attain a minimum at a finite value. Optimal speed and number of chasing hounds exist and depend on each chaser's baseline power consumption. The model can serve as an analytically tractable basis for further studies with bearing on the growing field of smart microswimmers and autonomous robots.

Diffusive transport on networks with stochastic resetting to multiple nodes

We study the diffusive transport of Markovian random walks on arbitrary networks with stochastic resetting to multiple nodes. We deduce analytical expressions for the stationary occupation probability and for the mean and global first passage times. This general approach allows us to characterize the effect of resetting on the capacity of random walk strategies to reach a particular target or to explore the network. Our formalism holds for ergodic random walks and can be implemented from the spectral properties of the random walk without resetting, providing a tool to analyze the efficiency of search strategies with resetting to multiple nodes. We apply the methods developed here to the dynamics with two reset nodes and derive analytical results for normal random walks and Lévy flights on rings. We also explore the effect of resetting to multiple nodes on a comb graph, Lévy flights that visit specific locations in a continuous space, and the Google random walk strategy on regular networks.

Mean encounter times for multiple random walkers on networks

We introduce a general approach for the study of the collective dynamics of noninteracting random walkers on connected networks. We analyze the movement of R independent (Markovian) walkers, each defined by its own transition matrix. By using the eigenvalues and eigenvectors of the R independent transition matrices, we deduce analytical expressions for the collective stationary distribution and the average number of steps needed by the random walkers to start in a particular configuration and reach specific nodes the first time (mean first-passage times), as well as global times that characterize the global activity. We apply these results to the study of mean first-encounter times for local and nonlocal random walk strategies on different types of networks, with both synchronous and asynchronous motion.

Diffusion–Advection Equations on a Comb: Resetting and Random Search

This review addresses issues of various drift–diffusion and inhomogeneous advection problems with and without resetting on comblike structures. Both a Brownian diffusion search with drift and an inhomogeneous advection search on the comb structures are analyzed. The analytical results are verified by numerical simulations in terms of coupled Langevin equations for the comb structure. The subordination approach is one of the main technical methods used here, and we demonstrated how it can be effective in the study of various random search problems with and without resetting.

First-encounter time of two diffusing particles in confinement

We investigate how confinement may drastically change both the probability density of the first-encounter time and the associated survival probability in the case of two diffusing particles. To obtain analytical insights into this problem, we focus on two one-dimensional settings: a half-line and an interval. We first consider the case with equal particle diffusivities, for which exact results can be obtained for the survival probability and the associated first-encounter time density valid over the full time domain. We also evaluate the moments of the first-encounter time when they exist. We then turn to the case with unequal diffusivities and focus on the long-time behavior of the survival probability. Our results highlight the great impact of boundary effects in diffusion-controlled kinetics even for simple one-dimensional settings, as well as the difficulty of obtaining analytic results as soon as the translational invariance of such systems is broken.