arXiv subjects

Alberto Rosso

Publications and source records attributed to Alberto Rosso.

At least 19 recordsLinked to original sources

arXiv2026

Splitting probabilities for Brownian motion with diffusing boundaries: Application to polymer translocation

We study the translocation of a polymer chain through a nanopore where the chain length fluctuates stochastically due to the polymerization-depolymerization processes at the chain ends. We map this process to an equivalent representation where the pore performs a stochastic random-walk-like process on a line in the presence of two diffusing sinks on either side of it with diffusion constants $D_1$ and $D_3$ respectively. The translocation process terminates when the pore hits either of the two outer diffusing sinks. In the case where the pore motion itself is diffusive with diffusion constant $D_2$, we compute exactly the splitting probability that the pore hits the left (right) sink before hitting the right (left) sink. We show that the splitting probability in the presence of mobile sinks is rather nontrivial compared to the classical case of immobile sinks (the latter corresponds to the case when the chain length is fixed). Furthermore, we also compute exactly the probability distribution of the translocation time and that of the chain length at the completion time of the translocation. We show that both distributions have power law tails with exponents that depend continuously on the diffusion constants $D_1$, $D_2$ and $D_3$. We validate our analytical predictions via numerical simulations. We then present numerical results for the case when the pore performs a fractional Brownian motion with Hurst exponent $0<H<1$, while the sinks are still diffusive.

cond-mat.stat-mech↗

arXiv2026

Contrastive Regularization of Machine Learning Potentials

Machine learning interatomic potentials are trained to predict energies and forces but built to be sampled: their purpose is to drive molecular simulations whose observables average over the equilibrium distribution the potential defines. They exemplify a broader AI problem -- learned regressors deployed as generators -- where pointwise accuracy does not guarantee a correct distribution. We show that potentials trained by standard Mean Squared Error (MSE) minimization on Density Functional Theory (DFT) data can reach chemical accuracy on held-out data, yet still fail as samplers: their trajectories drift into spurious low-energy minima and return thermodynamic observables that depart sharply from the reference. To correct this, we introduce Contrastive Regularized MSE (CRMSE), a post-training step that augments the MSE with a contrastive term derived from the Kullback--Leibler divergence between the potential's implicit Boltzmann distribution and the target. The network serves as its own energy-based model: persistent Langevin chains expose the configurations it drifts into and raise their energy, adding no new ab initio data. On the ethanol and aspirin molecules of the MD17 dataset, CRMSE confines the sampler to the physical basin and recovers the energy distribution, interatomic-distance distributions, and dihedral free-energy profiles to near-quantitative agreement with DFT, while preserving force accuracy and keeping energy errors within chemical accuracy; it remains effective when the training set is sharply reduced. That MSE training fails this way on MD17 -- one of the most widely used benchmarks -- while a minimal contrastive correction repairs it suggests that reliable sampling depends less on data volume than on training the model against the distribution it produces: distribution-level training is not a refinement of regression accuracy, but a distinct requirement.

physics.chem-ph↗

arXiv2026

Single-worldline theory for a dissipative Mott transition

In one dimension at zero temperature, local baths with spectral exponent $s$ can stabilize a compressible, non-superfluid dissipative phase between the Mott insulator and Luttinger liquid. The dissipative-to-Mott transition is accessible neither to perturbative renormalization-group methods nor to the free-fermion description of the conventional Mott transition. Here, we show it is governed by the worldline of a single doped excitation, whose geometrical roughness determines the critical exponents. Without dissipation, this worldline undergoes Brownian motion, recovering $\beta=\nu=1/z=1/2$. Dissipation turns it into a long-range interacting interface, yielding continuously varying exponents $\beta=\nu=1/z=s-1$ for $1<s<3/2$, and $\beta=\nu=1/z=0$ for $s<1$. The same theory identifies $s=3/2$ as the threshold above which the dissipative phase disappears. Large-scale Monte Carlo simulations of both the single-worldline theory and the original many-body model quantitatively support these predictions.

cond-mat.str-el↗

arXiv2026

Activation and Avalanche Length Scales in the Finite-Temperature Creep of an Elastic Interface

We investigate the creep dynamics of a driven elastic line at finite temperature, well below the depinning threshold. We show that creep is governed by two distinct length scales. The first, $\ell_{\mathrm{opt}}$, corresponds to the optimal activated rearrangements that control the dynamics' bottleneck and remains essentially temperature-independent. The second, $\ell_{\mathrm{av}}$, characterizes the spatial extent of thermally activated avalanches and grows as temperature decreases. By combining structural and dynamical observables, we show that $\ell_{\mathrm{av}}$ governs both the crossover in the structure factor and the growth of the four-point dynamical susceptibility, while the relaxation time remains controlled by activation over large barriers associated with $\ell_{\mathrm{opt}}$. We find that the avalanche scale follows $\ell_{\mathrm{av}}(T)\sim T^{-\nu_{\mathrm{dep}}}$, thereby selecting a unique scenario among competing theoretical predictions. These results establish a unified picture of finite-temperature creep in which activation controls temporal scales while depinning criticality governs spatial correlations.

cond-mat.stat-mech↗

arXiv2026

Flow of yield stress fluid in a percolating network

We study the flow of a Bingham yield-stress fluid in a pore network model where the throats have radii drawn from a uniform distribution. We consider the case in which a fraction of the largest radii is blocked. The fluid can flow only through the percolating cluster that exists when the fraction is above the percolation threshold. Two distinct flow regimes are identified: above the percolation threshold the flow curve can be characterized by deterministic values of the critical pressure drop, permeability, and other observables, with subleading fluctuations that we quantify. At the percolation threshold these quantities become non-self-averaging, and their scaling is governed exclusively by the critical percolation backbone, independent of the specific realization of the radii.

physics.flu-dyn↗

arXiv2025

Ergodicity Breaking in Active Run-and-Tumble Particles in a Double-Well Potential

We investigate the dynamics of a run-and-tumble particle in a double-well potential and demonstrate that, in stark contrast to Brownian particles, active dynamics can lead to strong ergodicity breaking. When the barrier height exceeds a critical threshold, the long-time position distribution depends crucially on the initial condition: if the particle starts within the basin of attraction of one well, it remains trapped there, while if it begins between the two basins, it can reach either well with a finite probability, which we compute exactly via hitting probabilities. Below the critical barrier height, ergodicity is restored and the system converges to a unique stationary distribution, which we derive analytically. Using this result, we also estimate the characteristic barrier crossing time and show that it violates Kramer's-Arrhenius law, and displays a divergence near the critical height following a Vogel-Fulcher-Tammann-like form with an anomalous exponent $1/2$.

cond-mat.stat-mech↗

arXiv2025

Emergence of Nonequilibrium Latent Cycles in Unsupervised Generative Modeling

We show that nonequilibrium dynamics can play a constructive role in unsupervised machine learning by inducing the spontaneous emergence of latent-state cycles. We introduce a model in which visible and hidden variables interact through two independently parametrized transition matrices, defining a Markov chain whose steady state is intrinsically out of equilibrium. Likelihood maximization drives this system toward nonequilibrium steady states with finite entropy production, reduced self-transition probabilities, and persistent probability currents in the latent space. These cycles are not imposed by the architecture but arise from training, and models that develop them reproduce the empirical distribution of data classes more faithfully, with a clear correlation between agreement with the data and entropy production. Compared with equilibrium approaches such as restricted Boltzmann machines, our model breaks the detailed balance between the forward and backward conditional transitions and relies on a log-likelihood gradient that depends explicitly on the last two steps of the Markov chain. Hence, this exploration of the interface between nonequilibrium statistical physics and modern machine learning suggests that introducing irreversibility into latent-variable models can improve the fidelity of the generated data distribution.

cond-mat.stat-mech↗

arXiv2025

The generic Mott transition in the sine-Gordon model through an embedded worm algorithm

The generic Mott transition in one-dimensional quantum systems can be described by the sine-Gordon model with a tilt via bosonization. Because the configuration space of the sine-Gordon model separates into distinct topological sectors, standard local Monte Carlo schemes are limited to very small system sizes. To overcome this limitation, we introduce the smooth worm (SmoWo) Monte Carlo algorithm which enlarges the configuration space to allow smooth transitions between topological sectors. The method combines worm updates with event-chain Monte Carlo moves. We explicitly prove its validity and quantify its performance. Thanks to the substantial acceleration achieved by the SmoWo algorithm, we are able to simulate large system sizes, providing a precise picture of the different phases and critical behaviour of the sine-Gordon model.

cond-mat.str-el↗

arXiv2025

Anomalous Critical Behavior of Driven Disordered Systems Beyond the Overdamped Limit

We investigate the role of relaxation mechanisms in the driven response of elastic disordered interfaces in finite dimensions, focusing on the interplay between dimensionality and interaction range. Through extensive numerical simulations, we identify two distinct dynamical regimes. In two-dimensional systems with long-range interactions, we observe a regime of coexistence between pinned and flowing states. In contrast, for one-dimensional interfaces with long-range elasticity, as well as for short-range interactions in both 1D and 2D, the coexistence regime is absent. Nevertheless, the avalanche statistics differ significantly from those of overdamped systems: the usual power-law distribution is replaced by a pronounced bump, associated with large, anomalous avalanches that expand ballistically. We interpret these events as failed synchronization attempts and suggest they could be detected in experimental systems.

cond-mat.dis-nn↗

arXiv2025

Discontinuity in the distribution of field increments between avalanches in non-abelian random field Blume-Emery-Griffiths model with no passing violation

We study the zero-temperature quasi-statically driven dynamics of the random field Blume--Emery--Griffiths model (RFBEGM) as a minimal framework to investigate the consequences of violating the no-passing property in driven disordered systems. While the random field Ising model obeys no-passing and displays abelian relaxation dynamics, we show that this property is generically violated in the RFBEGM. By systematically exploring the full parameter space of the fully connected model, we identify the regimes in which no-passing is broken and demonstrate that, when this violation is combined with frustration induced by a repulsive biquadratic coupling, it leaves a clear dynamical signature. Specifically, the distribution of the minimal field increment required to trigger successive avalanches develops a discontinuity that is absent both in no-passing dynamics and in unfrustrated no-passing-violating regimes. We provide analytical arguments that locate the onset of this discontinuity, in excellent agreement with numerical simulations. Our results establish this discontinuity as a robust diagnostic of frustration-induced blocking in non-abelian avalanche dynamics within a mean-field setting, without making claims about new universality classes.

cond-mat.stat-mech↗

arXiv2025

Uncertainty in AI-driven Monte Carlo simulations

In the study of complex systems, evaluating physical observables often requires sampling representative configurations via Monte Carlo techniques. These methods rely on repeated evaluations of the system's energy and force fields, which can become computationally expensive. To accelerate these simulations, deep learning models are increasingly employed as surrogate functions to approximate the energy landscape or force fields. However, such models introduce epistemic uncertainty in their predictions, which may propagate through the sampling process and affect the simulation's macroscopic behavior. In our work, we present the Penalty Ensemble Method (PEM) to quantify epistemic uncertainty and mitigate its impact on Monte Carlo sampling. Our approach introduces an uncertainty-aware modification of the Metropolis acceptance rule, which increases the rejection probability in regions of high uncertainty, thereby enhancing the reliability of the simulation outcomes.

cond-mat.dis-nn↗

arXiv2025

Extreme value statistics in a continuous time branching process: a pedagogical primer

We study a continuous time branching process where an individual splits into two daughters with rate b and dies with rate a, starting from a single individual at t=0. We show that the model can be mapped exactly to a random walk problem where the population size N(t) performs a random walk on a positive semi-infinite lattice. The hopping rate of this random walker out of a site labelled n is proportional to n, i.e., the walker gets more and more `agitated' as it moves further and further away from the origin--we call this an `agitated random walk' (ARW). We demonstrate that this random walk problem is particularly suitable to obtain exact explicit results on the extreme value statistics, namely, on the distribution of the maximal population size M(t)= \max_{0\tau\le t}[N(\tau)] up to time t. This extreme value distribution displays markedly different behaviors in the three phases: (i) subcritical (b a). In the subcritical and critical phases , Q(L,t) becomes independent of time t for large t and the stationary distribution Q(L, \infty) decays to zero with increasing L, respectively exponentially (subcritical) and algebraically (critical). For finite but large t, the distribution at the critical point exhibits a scaling form Q(L,t)\sim f_c(L/{at})/L^2 where the scaling function f_c(z) has a nontrivial shape that we compute analytically. In the supercritical phase, the distribution Q(L,t) has a `fluid' part that becomes independent of t for large t and a `condensate' part (a delta peak centered at e^{(b-a)t}) which gets disconnected from the `fluid' part and moves rapidly to \infty as time increases. We also verify our analytical predictions via numerical simulations finding excellent agreement.

cond-mat.stat-mech↗

arXiv2025

Bosonized one-dimensional quantum systems through enhanced event-chain Monte Carlo

We design an enhanced Event-Chain Monte Carlo algorithm to study 1D quantum dissipative systems, using their bosonized representation. Expressing the bosonized Hamiltonian as a path integral over a scalar field enables the application of Monte Carlo algorithms developed for classical systems. Specifically, we focus on a dissipative XXZ spin chain, exhibiting critical slowing down, minima degeneracy and long-range interactions. Addressing all three bottlenecks, we design an algorithm that combines local persistent Event-Chain Monte Carlo moves with global cluster moves, in a O(1)-complexity implementation. Through systematic performance analysis, we show that such an algorithm outperforms traditional Metropolis algorithms by more than a magnitude factor and is competitive with current state-of-the-art Quantum Monte Carlo algorithms. We then use this approach to determine the dissipative spin chain's phase diagram, thereby reinforcing prior analytical predictions.

cond-mat.str-el↗

arXiv2025

The stochastic porous medium equation in one dimension

We study the porous medium equation (PME) in one space dimension in presence of additive non-conservative white noise, and interpreted as a stochastic growth equation for the height field of an interface. We predict the values of the two growth exponents $\alpha$ and $\beta$ using the functional RG. Extensive numerical simulations show agreement with the predicted values for these exponents, however they also show anomalous scaling with an additional "local" exponent $\alpha_{\rm loc}$, as well as multiscaling originating from broad distributions of local height differences. The stationary measure of the stochastic PME is found to be well described by a random walk model, related to a Bessel process. This model allows for several predictions about the multiscaling properties.

cond-mat.stat-mech↗

arXiv2024

Triplets of local minima in a high-dimensional random landscape: Correlations, clustering, and memoryless activated jumps

We compute the distribution of triplets of stationary points in the energy landscape of the spherical p-spin model, by evaluating the quenched three-point complexity by means of the Kac-Rice formalism. We show the occurrence of transitions in the organization of stationary points in the landscape, identifying regions where local minima and saddles accumulate and cluster around other stationary points, thus displaying the presence of correlations in the landscape. We discuss the implications of these findings for the dynamical exploration of the energy landscape in the activated regime, specifying conditions under which transitions between local minima are expected to exhibit correlated rates and when, conversely, activated jumps are likely to be memoryless.

cond-mat.dis-nn↗

arXiv2024

Numerical study of Darcy's law of yield stress fluids on a deep tree-like network

Understanding the flow dynamics of yield stress fluids in porous media presents a substantial challenge. Both experiments and extensive numerical simulations frequently show a non-linear relationship between the flow rate and the pressure gradient, deviating from the traditional Darcy law. In this article, we consider a tree-like porous structure and utilize an exact mapping with the directed polymer (DP) with disordered bond energies on the Cayley tree. Specifically, we adapt an algorithm recently introduced by Brunet et al. [Europhys. Lett. 131, 40002 (2020)] to simulate exactly the tip region of branching random walks with the help of a spinal decomposition, to accurately compute the flow on extensive trees with several thousand generations. Our results confirm the asymptotic predictions proposed by Schimmenti et al. [Phys. Rev. E 108, L023102 (2023)], tested therein only for moderate trees of about 20 generations.

cond-mat.dis-nn↗

arXiv2024

Importance Sampling for counting statistics in one-dimensional systems

In this paper, we consider the problem of numerical investigation of the counting statistics for a class of one-dimensional systems. Importance sampling, the cornerstone technique usually implemented for such problems, critically hinges on selecting an appropriate biased distribution. While exponential tilt in the observable stands as the conventional choice for various problems, its efficiency in the context of counting statistics may be significantly hindered by the genuine discreteness of the observable. To address this challenge, we propose an alternative strategy which we call importance sampling with the local tilt. We demonstrate the efficiency of the proposed approach through the analysis of three prototypical examples: a set of independent Gaussian random variables, Dyson gas, and Symmetric Simple Exclusion Process (SSEP) with a steplike initial condition.

cond-mat.stat-mech↗

arXiv2024

Dynamical heterogeneities of thermal creep in pinned interfaces

Disordered systems under applied loading display slow creep flows at finite temperature, which can lead to the material rupture. Renormalization group arguments predicted that creep proceeds via thermal avalanches of activated events. Recently, thermal avalanches were argued to control the dynamics of liquids near their glass transition. Both theoretical approaches are markedly different. Here we provide a scaling description that seeks to unify dynamical heterogeneities in both phenomena, confirm it in simple models of pinned elastic interfaces, and discuss its experimental implications.

cond-mat.dis-nn↗