arXiv subjects

Arnab Pal

Publications and source records attributed to Arnab Pal.

At least 19 recordsLinked to original sources

arXiv2026

Time-delayed feedback turns Arrhenius escape logarithmic

Thermal escape is governed by the Arrhenius law, where the mean escape time scales exponentially with the barrier height. We show that the non-Markovianity induced by time-delayed feedback in the confining force removes this exponential scaling. Beyond a threshold set by the curvature of the minimum, the delay destabilizes the well, and the thermal noise seeds an instability that is subsequently amplified deterministically to the boundary leading to \textit{slingshot} escape trajectories. The escape time becomes logarithmic in the barrier, its fluctuations follow a Gumbel law, and an optimal delay enables escape faster than free diffusion. Our results propose time delay as a tunable and experimentally feasible control parameter for accelerating activated processes.

cond-mat.stat-mech↗

arXiv2026

Mpemba effect in a chemomechanical model of the Kinesin molecular motor

The Mpemba effect, wherein a system prepared farther from equilibrium relaxes faster than one initially closer to equilibrium, has been extensively investigated in a wide range of physical systems. In contrast, its role in biologically relevant non-equilibrium processes remains largely unexplored. Here, we investigate anomalous relaxation in the six-state chemomechanical network model of the Kinesin molecular motor under both equilibrium and non-equilibrium conditions. We first establish the existence of the Mpemba effect in chemical equilibrium and show that many of its qualitative features can be understood from the underlying free-energy landscape. We then examine the effects of mechanical and chemical driving, showing that breaking detailed balance primarily reshapes the Mpemba phase diagram without qualitatively altering the relaxation phenomenology over the physically relevant parameter regime. Finally, we demonstrate that the relaxation of the motor velocity also mirrors the anomalous relaxation of the underlying stochastic dynamics, thereby identifying an experimentally accessible signature of the Mpemba effect. Our results establish molecular motors as a promising baseline for studying anomalous relaxation in living systems and suggest a broader framework for exploring the Mpemba effect in non-equilibrium biochemical networks.

cond-mat.stat-mech↗

arXiv2026

Geometric Brownian motion with intermittent entries and exits

We study a generalized geometric Brownian motion framework that incorporates both entries of new units and exit mechanisms for the current population, extending earlier stochastic resetting models where these rates are treated as identical. The model captures realistic features observed in many economic observables, which can be explained as market-driven firm entries/exits, worker inflow/outflow, and income growth/loss. This model is not conservative and, despite the asymmetry in the entry and exit rates, we find that the system eventually relaxes to a stationary distribution. Moreover, our analysis reveals three distinct dynamical regimes in the moments of the distribution, arising from the interplay between volatility, drift, entry, and exit rates. We further derive the survival probability and the mean first-passage time associated with the observed variable reaching certain threshold under the competing entry-exit processes. Interestingly, we identify an optimal exit rate that minimizes the mean first-passage time, providing insights into how entry and exit policies can influence the outcome of the system. These results should be useful for understanding the long-run behavior of economic systems in which growth, volatility, entry, and exit jointly shape the evolution of heterogeneous units.

econ.GN↗

arXiv2026

Resetting optimized competitive first-passage outcomes in non-Markovian systems

We investigate the role of stochastic resetting in non-Markovian systems, where memory effects arise due to slow relaxation, rugged energy landscapes, disordered environments, and molecular crowding. Using the celebrated continuous-time random walk (CTRW) framework, we analyze first-passage processes with multiple competing outcomes and examine how resetting can selectively enhance desired events. We characterize the efficiency of resetting through conditional mean first-passage times (MFPTs) and demonstrate that its impact is highly sensitive to the underlying waiting-time statistics. Furthermore, we derive an inequality that quantifies how resetting controls fluctuations in conditional first-passage times (FPTs), revealing regimes where variability is significantly suppressed. Our results provide a systematic understanding of how long-term memory influences competitive first-passage outcomes and establish resetting as a powerful control mechanism beyond the conventional Markovian setting.

cond-mat.stat-mech↗

arXiv2026

Optimal threshold resetting in collective diffusive search

Stochastic resetting has attracted significant attention in recent years due to its wide-ranging applications across physics, biology, and search processes. In most existing studies, however, resetting events are governed by an external timer and remain decoupled from the system's intrinsic dynamics. In a recent Letter by Biswas et al, we introduced threshold resetting (TR) as an alternative, event-driven optimization strategy for target search problems. Under TR, the entire process is reset whenever any searcher reaches a prescribed threshold, thereby coupling the resetting mechanism directly to the internal dynamics. In this work, we study TR-enabled search by $N$ non-interacting diffusive searchers in a one-dimensional box $[0,L]$, with the target at the origin and the threshold at $L$. By optimally tuning the scaled threshold distance $u = x_0/L$, the mean first-passage time can be significantly reduced for $N \geq 2$. We identify a critical population size $N_c(u)$ below which TR outperforms reset-free dynamics. Furthermore, for fixed $u$, the mean first-passage time depends non-monotonically on $N$, attaining a minimum at $N_{\mathrm{opt}}(u)$. We also quantify the achievable speed-up and analyze the operational cost of TR, revealing a nontrivial optimization landscape. These findings highlight threshold resetting as an efficient and realistic optimization mechanism for complex stochastic search processes.

cond-mat.stat-mech↗

arXiv2026

Fully Distributed Adaptive Consensus Approach for Economic Dispatch Problem

This research presents a novel approach to solving the economic load dispatch (ELD) problem in smart grid systems by leveraging a multi-agent distributed consensus strategy. The core idea revolves around achieving agreement among generators on their incremental cost values, thereby enabling an optimal allocation of power generation. To enhance convergence and robustness, the study introduces an adaptive coupling weight mechanism within a fully decentralized consensus framework, carefully designed with appropriate initial settings for incremental costs. The proposed distributed control protocol is versatile it functions effectively in both constrained and unconstrained generator capacity scenarios. Importantly, the methodology ensures that total power generation continuously matches dynamic load demands throughout the dispatch process, maintaining system-wide balance. To accommodate fluctuating and time varying load profiles, a dummy node is incorporated into the network architecture, acting as a flexible proxy for real time demand changes. The resilience of the method is further evaluated under communication disruptions, specifically by analyzing generator link failures through a switching network topology. Stability of the system is rigorously established using a Lyapunov-based analysis, assuming an undirected and connected communication graph among agents. To validate the practical efficacy of the proposed technique, comprehensive simulations are conducted on the IEEE 30 bus test system within the MATLAB environment, confirming its accuracy, adaptability, and computational efficiency in realistic smart grid conditions.

eess.SY↗

arXiv2025

Emulating microbial run-and-tumble and tactic motion by stochastically reorienting synthetic active Brownian particles

Replicating efficient and adaptable microbial navigation strategies, such as run and tumble (RnT) and tactic motions to synthetic active agents has been an enduring quest. To this end, we introduce a stochastic orientational reset (SOR) protocol, in which the propulsion direction of an active Brownian particle (ABP) is reassigned to a random orientation within a defined reset-cone. When the reset-cone is aligned with the instantaneous propulsion direction, ABPs reproduce the RnT dynamics of E. coli; when set along an attractant gradient, they exhibit taxis - with extensive adaptability in persistence through the angular width of the reset-cone and reset rate. We establish the robustness of this protocol across a broad range of swimming speeds using experiments, simulations, and analytical theory.

cond-mat.soft↗

arXiv2025

Speeding up Brownian escape via intermediate finite potential barriers

The mean first-passage time (MFPT) for a Brownian particle to surmount a potential barrier of height $\Delta U$ is a fundamental quantity governing a wide array of physical and chemical processes. According to the Arrhenius Law, the MFPT typically grows exponentially with increasing barrier height, reflecting the rarity of thermally activated escape events. In this work, we demonstrate that the MFPT can be significantly reduced by reshaping the original single-barrier potential into a structured energy landscape comprising multiple intermediate barriers of lower heights, while keeping the total barrier height $\Delta U$ unchanged. Furthermore, this counterintuitive result holds across both linear and nonlinear potential profiles. Our findings suggest that tailoring the energy landscape -- by introducing well-placed intermediate barriers -- can serve as an effective control strategy to accelerate thermally activated transitions. These predictions are amenable to experimental validation using optical trapping techniques.

cond-mat.stat-mech↗

arXiv2025

Diffusion in a wedge geometry: First-Passage Statistics under Stochastic Resetting

We study the diffusion process in the presence of stochastic resetting inside a two-dimensional wedge of top angle $\alpha$, bounded by two infinite absorbing edges. In the absence of resetting, the second moment of the first-passage time diverges for $\alpha>\pi/4$ while it remains finite for $\alpha<\pi/4$, resulting in an unbounded or bounded coefficient of variation in the respective angular regimes. Upon introducing stochastic resetting, we analyze the first-passage properties in both cases and identify the geometric configurations in which resetting consistently enhances the rate of absorption or escape through the boundaries. By deriving the expressions for the probability currents and conditional first-passage quantities such as splitting probabilities and conditional mean first-passage times, we demonstrate how resetting can be employed to bias the escape pathway through the favorable boundary. Our theoretical predictions are verified through Langevin-type numerical simulations, showing excellent agreement.

cond-mat.stat-mech↗

arXiv2025

Target search optimization by threshold resetting

We introduce a new class of first passage time optimization driven by threshold resetting, inspired by many natural processes where crossing a critical limit triggers failure, degradation or transition. In here, search agents are collectively reset when a threshold is reached, creating event-driven, system-coupled simultaneous resets that induce long-range interactions. We develop a unified framework to compute search times for these correlated stochastic processes, with ballistic- and diffusive searchers as key examples uncovering diverse optimization behaviors. A cost function, akin to breakdown penalties, reveals that optimal resetting can forestall larger losses. This formalism generalizes to broader stochastic systems with multiple degrees of freedom.

cond-mat.stat-mech↗

arXiv2025

Universal criterion for selective outcomes under stochastic resetting

Resetting plays a pivotal role in optimizing the completion time of complex first passage processes with single or multiple outcomes/exit possibilities. While it is well established that the coefficient of variation -- a statistical dispersion defined as a ratio of the fluctuations over the mean of the first passage time -- must be larger than unity for resetting to be beneficial for any outcome averaged over all the possibilities, the same can not be said while conditioned on a particular outcome. The purpose of this letter is to derive a universal condition which reveals that two statistical metric -- the mean and coefficient of variation of the conditional times -- come together to determine when resetting can expedite the completion of a selective outcome, and furthermore can govern the biasing between preferential and non-preferential outcomes. The universality of this result is demonstrated for a one dimensional diffusion process subjected to resetting with two absorbing boundaries.

cond-mat.stat-mech↗

arXiv2024

Inferring intermediate states by leveraging the many-body Arrhenius law

Metastable states appear as long-lived intermediate states in various natural transport phenomena which are governed by energy landscapes. As such, these intermediate metastable states dominate the system's dynamics at coarse grained times. Moreover, they can strongly influence the overall pathways through which the energy landscape is explored. Thus, quantifying these metastabilities is crucial for uncovering the key details of the underlying landscape. Here, we introduce a robust method based on a generalized many-body Arrhenius law to identify metastable states in escape problems involving interacting particles with excluded volume. Experimental platforms such as colloidal transport or macromolecular translocation through biological pores can offer promising settings to validate our predictions.

cond-mat.stat-mech↗

arXiv2024

A resetting particle embedded in a viscoelastic bath

We examine the behavior of a colloidal particle immersed in a viscoelastic bath undergoing stochastic resetting at a rate $r$. Microscopic probes suspended in viscoelastic environment do not follow the classical theory of Brownian motion. This is primarily because the memory from successive collisions between the medium particles and the probes does not necessarily decay instantly as opposed to the classical Langevin equation. To treat such a system one needs to incorporate the memory effects to the Langevin equation. The resulting equation formulated by Kubo, known as the Generalized Langevin equation (GLE), has been instrumental to describe the transport of particles in inhomogeneous or viscoelastic environments. The purpose of this work, henceforth, is to study the behavior of such a colloidal particle governed by the GLE under resetting dynamics. To this end, we extend the renewal formalism to compute the general expression for the position variance and the correlation function of the resetting particle driven by the environmental memory. These generic results are then illustrated for the prototypical example of the Jeffreys viscoelastic fluid model. In particular, we identify various timescales and intermittent plateaus in the transient phase before the system relaxes to the steady state; and further discuss the effect of resetting pertaining to these behaviors. Our results are supported by numerical simulations showing an excellent agreement.

cond-mat.stat-mech↗

arXiv2024

Occupation time statistics for non-Markovian random walks

We study the occupation time statistics for non-Markovian random walkers based on the formalism of the generalized master equation for the Continuous-Time Random Walk. We also explore the case when the random walker additionally undergoes a stochastic resetting dynamics. We derive and solve the backward Feynman-Kac equation to find the characteristic function for the occupation time in an interval and for the half occupation time in the semi-infinite domain. We analyze the behaviour of the PDFs, the moments, the limiting distributions and the ergodic properties for both occupation times when the underlying random walk is normal or anomalous. For the half occupation time, we revisit the famous arcsine law and examine its validity pertaining to various regimes of the rest period of the walker. Our results have been verified with numerical simulations exhibiting an excellent agreement.

cond-mat.stat-mech↗

arXiv2024

The impact of stochastic resetting on resource allocation: The case of Reallocating geometric Brownian motion

We study the effects of stochastic resetting on the Reallocating geometric Brownian motion (RGBM), an established model for resource redistribution relevant to systems such as population dynamics, evolutionary processes, economic activity, and even cosmology. The RGBM model is inherently non-stationary and non-ergodic, leading to complex resource redistribution dynamics. By introducing stochastic resetting, which periodically returns the system to a predetermined state, we examine how this mechanism modifies RGBM behavior. Our analysis uncovers distinct long-term regimes determined by the interplay between the resetting rate, the strength of resource redistribution, and standard geometric Brownian motion parameters: the drift and the noise amplitude. Notably, we identify a critical resetting rate beyond which the self-averaging time becomes effectively infinite. In this regime, the first two moments are stationary, indicating a stabilized distribution of an initially unstable, mean-repulsive process. We demonstrate that optimal resetting can effectively balance growth and redistribution, reducing inequality in the resource distribution. These findings help us understand better the management of resource dynamics in uncertain environments.

cond-mat.stat-mech↗

arXiv2024

Channel-facilitated transport under resetting dynamics

The transport of particles through channels holds immense significance in physics, chemistry, and biological sciences. For instance, the motion of solutes through biological channels is facilitated by specialized proteins that create water-filled channels and valuable insights can be obtained by studying the transition paths of particles through a channel and gathering statistics on their lifetimes within the channel or their exit probabilities. In a similar vein, we consider a one-dimensional model of channel-facilitated transport where a diffusive particle is subject to attractive interactions with the walls within a limited region of the channel. We study the statistics of conditional and unconditional escape times, in the presence of resetting--an intermittent dynamics that brings the particle back to its initial coordinate randomly. We determine analytically the physical conditions under which such resetting mechanism can become beneficial for faster escape of the particles from the channel thus enhancing the transport. Our theory has been verified with the aid of Brownian dynamics simulations for various interaction strengths and extent. The overall results presented herein highlight the scope of resetting-based strategies to be universally promising for complex transport processes of single or long molecules through biological membranes.

cond-mat.stat-mech↗

arXiv2024

Is active motion beneficial for target search with resetting in a thermal environment?

Stochastic resetting has recently emerged as an efficient target-searching strategy in various physical and biological systems. The efficiency of this strategy depends on the type of environmental noise, whether it is thermal or telegraphic (active). While the impact of each noise type on a search process has been investigated separately, their combined effects have not been explored. In this work, we explore the effects of stochastic resetting on an active system, namely a self-propelled run-and-tumble particle immersed in a thermal bath. In particular, we assume that the position of the particle is reset at a fixed rate with or without reversing the direction of self-propelled velocity. Using standard renewal techniques, we compute the mean search time of this active particle to a fixed target and investigate the interplay between active and thermal fluctuations. We find that the active search can outperform the Brownian search when the magnitude and flipping rate of self-propelled velocity are large and the strength of environmental noise is small. Notably, we find that the presence of thermal noise in the environment helps reduce the mean first passage time of the run-and-tumble particle compared to the absence of thermal noise. Finally, we observe that reversing the direction of self-propelled velocity while resetting can also reduce the overall search time.

cond-mat.stat-mech↗

arXiv2024

Drift-diffusive resetting search process with stochastic returns: speed-up beyond optimal instantaneous return

Stochastic resetting has emerged as a useful strategy to reduce the completion time for a broad class of first passage processes. In the canonical setup, one intermittently resets a given system to its initial configuration only to start afresh and continue evolving in time until the target goal is met. This is, however, an instantaneous process and thus less feasible for any practical purposes. A crucial generalization in this regard is to consider a finite-time return process which has significant ramifications to the first passage properties. Intriguingly, it has recently been shown that for diffusive search processes, returning in finite but stochastic time can gain significant speed-up over the instantaneous resetting process. Unlike diffusion which has a diverging mean completion time, in this paper, we ask whether this phenomena can also be observed for a first passage process with finite mean completion time. To this end, we explore the set-up of a classical drift-diffusive search process in one dimension with stochastic resetting and further assume that the return phase is modulated by a potential $U(x)=\lambda |x|$ with $\lambda>0$. For this process, we compute the mean first passage time exactly and underpin its characteristics with respect to the resetting rate and potential strength. We find a unified phase space that allows us to explore and identify the system parameter regions where stochastic return supersedes over both the underlying process and the process under instantaneous resetting. Furthermore and quite interestingly, we find that for a range of parameters the mean completion time under stochastic return protocol can be reduced further than the \textit{optimally restarted} instantaneous processes. We thus believe that resetting with stochastic returns can serve as a better optimization strategy owing to its dominance over classical first passage under resetting.

cond-mat.stat-mech↗