arXiv subjects

Édgar Roldán

Publications and source records attributed to Édgar Roldán.

At least 19 recordsLinked to original sources

arXiv2026

Martingale theory for heat and phase-space contraction in heterogeneous diffusions

We investigate martingale properties of heat dissipated by generic non-equilibrium overdamped Langevin dynamics with multiple dimensions and transport coefficients that may depend explicitly on time and/or space. We find quantitative criteria that establish that heat dissipation may exhibit transient martingale (time-conserved), submartingale (time increasing), and supermartingale (time decreasing) properties in Langevin dynamics with drift coefficients with spatial non-linear dependencies. Furthermore, we reveal a tight link between the heat statistics at stopping (e.g. first-passage) times with that of a functional quantifying the phase-space contraction in the system dynamics. The theoretical results are used to predict the statistical properties of the heat dissipated by a spherical microscopic particle subject to gravity and double-layer forces and hydrodynamically interacting with a rigid wall, whose validity is confirmed by numerical simulations. These results extend the scope of stochastic energetics by revealing a non-trivial relation between heat fluctuations' statistical properties and the non-linear features of non-equilibrium processes.

cond-mat.stat-mech↗

arXiv2026

Quo vadis, stochastic thermodynamics?

Stochastic thermodynamics is a framework for describing non-equilibrium processes at the level of fluctuating trajectories, where the state of a system evolves as a stochastic time series, allowing thermodynamic quantities such as work, heat, and entropy production to be defined along individual realizations rather than at the ensemble level only. Over the past three decades, the field has yielded fundamental results, including fluctuation theorems and several universal bounds, such as thermodynamic uncertainty relations, speed limit theorems, and many others. Many of them have been tested on a range of experimental platforms. This Perspective reviews recent developments in stochastic thermodynamics that extend its scope beyond its traditional domains, including systems with memory and hidden degrees of freedom, microscopic approaches to interacting and active matter, and geometric formulations based on optimal transport. Next, the Perspective surveys the challenges that arise when applying these ideas to macroscopic and complex systems, where the link between statistical irreversibility and thermodynamic dissipation becomes less direct. Finally, emerging applications in non-physical contexts are highlighted, including computation, biological systems, and social dynamics. Transcending the traditional boundaries of physics, these developments catalyze an unorthodox framework to tackle the thermodynamics of complex systems.

cond-mat.stat-mech↗

arXiv2026

Poisson-shot-noise hybrid machines: efficiency and quasistatic divergence

We study stochastic models of a microscopic active heat engine, comprised of an overdamped Brownian particle trapped in a harmonic potential, and in simultaneous contact with thermal (passive) and athermal (active) baths. The interaction with the active bath is modeled as a stochastic force described by Poisson shot-noise (PSN) having a specified amplitude distribution. With analytical calculations and numerical simulations, we study the thermodynamic performance of the machine to quasistatic cyclic protocols analogous to those running two-stroke and Stirling-like engines. For specific parameter ranges, the thermodynamic behavior is that of a $\textit{hybrid machine}$, simultaneously operating as a heat engine with respect to the passive/active baths and as a refrigerator with respect to the passive/active baths. Focusing on the parameter region where the overall performance is such of an engine, we show that the average total extracted work per cycle divided by average total heat intake from the cold baths per cycle may surpass the Carnot efficiency associated with the temperature of the passive baths. Applying the second law for active heat engines, we focus on a bona fide efficiency (bounded by Carnot's efficiency) that incorporates an information-theoretic metric $\mathcal{I}-$ which we call $\textit{quasistatic divergence}-$ quantifying how distinguishable are the engine's statistics in the quasistatic limit with respect to a continually changing equilibrium distribution. We analyze, with theory and numerical simulations, how the PSN shot rate and the degree of non-Gaussianity in the particle position distribution influence the efficiency of the engine, and explore the correlation between non-Gaussianity and efficiency. Our findings reveal optimal PSN shot rates maximizing the engine's efficiency and an intriguing non-bijective relation between efficiency and kurtosis

cond-mat.stat-mech↗

arXiv2026

Pareto fronts and trade-off relations from exact multi-objective optimization of thermal machines

Thermal machines are physical systems that, when fueled by input energy, perform output tasks such as heat pumping or the production of work. Their performance is characterized with several, often competing quantities, such as power, efficiency, energy waste, and resilience to environmental noise. Multi-objective optimization provides a key tool to investigate the characterization of the best thermal machines operating in the irreversible linear-response regime. Here, we derive exact analytical parameterizations for the optimal (Pareto) fronts associated with any given choice of relative weights assigned to their mean extracted power $P$, efficiency $η$, entropy production $Σ$ and the amplitude of power fluctuations $σ^2_P$. The geometry of the front of endoreversible machines is universal: two-, three-, and four-objective trade-offs follow analytical formulae that do not depend on the value of any physical parameter of the machine. We show that such universal thermodynamic Pareto fronts also set quantitative fundamental limits for the performance of non-endoreversible machines. Furthermore, we demonstrate that our results apply to existing experimental data from different physical systems also beyond the linear regime, ranging from atomic to macroscopic scales, including single-atom engines, colloidal systems, macroscopic engines and power plants.

cond-mat.stat-mech↗

arXiv2026

Bringing calorimetry (back) to life

Micro-calorimetry offers significant potential as a quantitative method for studying the structure and function of biological systems, for instance, by probing the excess heat released by cellular or sub-cellular structures, isothermal or not, when external parameters change. We present the conceptual framework of nonequilibrium calorimetry, and as illustrations, we compute the heat capacity of biophysical models with few degrees of freedom related to ciliar motion (rowing model) and molecular motor motion (flashing ratchets). Our quantitative predictions reveal intriguing dependencies of the (nonequilibrium) heat capacity as a function of relevant biophysical parameters, which can even take negative values as a result of biological activity.

physics.bio-ph↗

arXiv2025

First-Passage-Time Asymmetry for Biased Run-and-Tumble Processes

We explore first-passage phenomenology for biased active processes with a renewal-type structure, focusing in particular on paradigmatic run-and-tumble models in both discrete and continuous state spaces. In general, we show there is no equality between distributions of conditional first-passage times to symmetric barriers positioned in and against the bias direction. However, we give conditions for such a duality to be restored asymptotically (in the limit of a large barrier distance) and highlight connections to the Gallavotti-Cohen fluctuation relation and the method of images. Our general trajectory arguments of first-passage-time distributions for asymmetric run-and-tumble processes to escape from an interval of arbitrary width are supported by exact analytical results, which we derive extending Montroll's defect technique. Furthermore, we quantify the degree of violation of first-passage duality using Kullback-Leibler divergence and signal-to-noise ratios associated with the first-passage times to the two barriers. We reveal an intriguing dependence of such measures of first-passage asymmetry on the underlying often hidden tumbling dynamics which may inspire inference techniques based on first-passage-time statistics in active systems.

cond-mat.stat-mech↗

arXiv2025

Exactly solvable diffusions from space-time transformations

We consider a general one-dimensional overdamped diffusion model described by the Itô stochastic differential equation (SDE) ${dX_t=μ(X_t,t)dt+σ(X_t,t)dW_t}$, where $W_t$ is the standard Wiener process. We obtain a specific condition that $μ$ and $σ$ must fulfil in order to be able to solve the SDE via mapping the generic process, using a suitable space-time transformation, onto the simpler Wiener process. By taking advantage of this transformation, we obtain the propagator in the case of open, reflecting, and absorbing \emph{time-dependent\/} boundary conditions for a large class of diffusion processes. In particular, this allows us to derive the first-passage time statistics of such a large class of models, some of which were so far unknown. While our results are valid for a wide range of non-autonomous, non-linear and non-homogeneous processes, we illustrate applications in stochastic thermodynamics by focusing on the propagator and first-passage-time statistics of isoentropic processes that were previously realized in the laboratory with Brownian particles trapped with optical tweezers.

cond-mat.stat-mech↗

arXiv2025

The need for statistical physics in Africa: perspective and an illustration in drug delivery problems

The development of statistical physics in Africa is in its nascent stages, yet its application holds immense promise for advancing emerging research trends on the continent. This perspective paper, a product of a two-week workshop on biophysics in Morogoro (Tanzania), aims to illuminate the potential of statistical physics in regional scientific research. We employ in-silico atomistic molecular dynamics simulations to investigate the loading and delivery capabilities of lecithin nanolipids for niclosamide, a poorly water-soluble drug. Our simulations reveal that the loading capacity and interaction strength between lecithin nanolipids and niclosamide improve with increased lecithin concentrations. We perform a free-energy landscape analysis which uncovers two distinct metastable conformations of niclosamide within both the aqueous phase and the lecithin nanolipids. Over a simulation period of half a microsecond, lecithin nanolipids self-assemble into a spherical monolayer structure, providing detailed atomic-level insights into their interactions with niclosamide. These findings underscore the potential of lecithin nanolipids as efficient drug delivery systems.

cond-mat.stat-mech↗

arXiv2025

Human perceptual decision making of nonequilibrium fluctuations

To better characterize the statistical processes underlying human decision-making, we performed experiments where human participants visualized fluctuations of physical nonequilibrium stationary states, and we analyzed responses in the context of stochastic thermodynamics. A total of forty five participants viewed hundreds of movies of a particle endowed with drifted Brownian dynamics and were tasked with judging the motion as leftward or rightward in a quick and reliable manner. Overall, the results uncover fundamental performance limits, consistent with recently established thermodynamic trade-offs (uncertainty relations, TURs) involving speed, accuracy, and dissipation; specifically, lower rates of entropy production lead to longer decision times. Moreover, to achieve a given level of observed accuracy, participants require more time than predicted by Wald's optimal sequential probability ratio test, indicating suboptimal integration of available information. In view of such suboptimality, we develop an alternative account equipped with non-Markovian evidence integration with a memory time constant, and find tight fits. Our results suggest that humans adapt their memory relaxation time to the rate of dissipation of the observed phenomenon, favouring memory over momentary evidence for effective decisions in scenarios where stimuli are far from equilibrium. Furthermore, we identify the effects of the environmental stability on decision-making performance and memory by comparing the results of the two sets of experiments: blocked (stationary) versus intermixed (non-stationary) conditions. Our study illustrates that perceptual psychophysics using stimuli rooted in nonequilibrium physical processes provides a robust platform for understanding how the human brain makes decisions on stochastic information inputs.

cond-mat.stat-mech↗

arXiv2025

Machine learning for cerebral blood vessels' malformations

Cerebral aneurysms and arteriovenous malformations are life-threatening hemodynamic pathologies of the brain. While surgical intervention is often essential to prevent fatal outcomes, it carries significant risks both during the procedure and in the postoperative period, making the management of these conditions highly challenging. Parameters of cerebral blood flow, routinely monitored during medical interventions or with modern noninvasive high-resolution imaging methods, could potentially be utilized in machine learning-assisted protocols for risk assessment and therapeutic prognosis. To this end, we developed a linear oscillatory model of blood velocity and pressure for clinical data acquired from neurosurgical operations. Using the method of Sparse Identification of Nonlinear Dynamics (SINDy), the parameters of our model can be reconstructed online within milliseconds from a short time series of the hemodynamic variables. The identified parameter values enable automated classification of the blood-flow pathologies by means of logistic regression, achieving an accuracy of 73 \%}. Our results demonstrate the potential of this model for both diagnostic and prognostic applications, providing a robust and interpretable framework for assessing cerebral blood vessel conditions.

cs.LG↗

arXiv2025

Active energy harvesting and work transduction by hair-cell bundles in bullfrog's inner ear

Hair cells actively drive oscillations of their mechanosensitive organelles--the hair bundles that enable hearing and balance sensing in vertebrates. Why and how some hair cells expend energy by sustaining this oscillatory motion in order to fulfill their function as signal sensors and others--as amplifiers, remains unknown. We develop a stochastic thermodynamic theory to describe energy flows in a periodically-driven hair bundle. Our analysis of thermodynamic fluxes associated with hair bundles' motion and external sinusoidal stimulus reveals that these organelles operate as thermodynamic work-to-work machines under different operational modes. One operational mode transduces the signal's power into the cell, whereas another allows the external stimulus to harvest the energy supplied by the cell. These two regimes might represent thermodynamic signatures of signal sensing and amplification respectively. In addition to work transduction and energy harvesting, our model also substantiates the capability of hair-cell bundles to operate as heaters and, at the expense of external driving, as active feedback refrigerators. We quantify the performance and robustness of the work-to-work conversion by hair bundles, whose efficiency in some conditions exceeds 80 % of the applied power.

cond-mat.stat-mech↗

arXiv2025

Universal relations and bounds for fluctuations in quasistatic small heat engines

The efficiency of any heat engine, defined as the ratio of average work output to heat input, is bounded by Carnot's celebrated result. However, this measure is insufficient to characterize the properties of miniaturized heat engines carrying non-negligible fluctuations, and a study of higher-order statistics of their energy exchanges is required. Here, we generalize Carnot's result for reversible cycles to arbitrary order moment of the work and heat fluctuations. Our results show that, in the quasistatic limit, higher-order statistics of a small engine's energetics depend solely on the ratio between the temperatures of the thermal baths. We further prove that our result for the second moment gives universal bounds for the ratio between the variances of work and heat for quasistatic cycles. We test this theory with our previous experimental results of a Brownian Carnot engine and observe the consistency between them, even beyond the quasistatic regime. Our results can be exploited in the design of thermal nanomachines to reduce their fluctuations of work output without marginalizing its average value and efficiency.

cond-mat.stat-mech↗

arXiv2024

Optimal switching strategies in multi-drug therapies for chronic diseases

Antimicrobial resistance is a threat to public health with millions of deaths linked to drug resistant infections every year. To mitigate resistance, common strategies that are used are combination therapies and therapy switching. However, the stochastic nature of pathogenic mutation makes the optimization of these strategies challenging. Here, we propose a two-scale stochastic model that considers the effective evolution of therapies in a multidimensional efficacy space, where each dimension represents the efficacy of a specific drug in the therapy. The diffusion of therapies within this space is subject to stochastic resets, representing therapy switches. The boundaries of the space, inferred from coarser pathogen-host dynamics, can be either reflecting or absorbing. Reflecting boundaries impede full recovery of the host, while absorbing boundaries represent the development of antimicrobial resistance, leading to therapy failure. We derive analytical expressions for the average absorption times, accounting for both continuous and discrete genomic changes using the frameworks of Langevin and Master equations, respectively. These expressions allow us to evaluate the relevance of times between drug-switches and the number of simultaneous drugs in relation to typical timescales for drug resistance development. We also explore realistic scenarios where therapy constraints are imposed to the number of administered therapies and/or their costs, finding non-trivial optimal drug-switching protocols that maximize the time before antimicrobial resistance develops while reducing therapy costs.

q-bio.PE↗

arXiv2024

Run-and-tumble exact work statistics in a lazy quantum measurement engine: stochastic information processing

We introduce a single-qubit quantum measurement engine fuelled by backaction energy input. To reduce energetic costs associated with information processing, the measurement outcomes are only used with a prescribed laziness probability in the feedback step. As a result, we show that the work extracted over consecutive cycles is a second-order Markov process, analogous to a run-and-tumble process with transient anomalous diffusion. We derive exact analytical expressions for the work finite-time moments and first-passage-time statistics. Furthermore, we find the optimal laziness probability maximizing the mean power extracted per cycle.

quant-ph↗

arXiv2024

Gambling Carnot Engine

We propose a theoretical model for a colloidal heat engine driven by a feedback protocol that is able to fully convert the net heat absorbed by the hot bath into extracted work. The feedback protocol, inspired by gambling strategies, executes a sudden quench at zero work cost when the particle position satisfies a specific first-passage condition. As a result, the engine enhances both power and efficiency with respect to a standard Carnot cycle, surpassing Carnot's efficiency at maximum power. Using first-passage and martingale theory, we derive analytical expressions for the power and efficiency far beyond the quasistatic limit and provide scaling arguments for their dependency with the cycle duration. Numerical simulations are in perfect agreement with our theoretical findings, and illustrate the impact of the data acquisition rate on the engine's performance.

cond-mat.stat-mech↗

arXiv2024

Is stochastic thermodynamics the key to understanding the energy costs of computation?

The relationship between the thermodynamic and computational characteristics of dynamical physical systems has been a major theoretical interest since at least the 19th century, and has been of increasing practical importance as the energetic cost of digital devices has exploded over the last half century. One of the most important thermodynamic features of real-world computers is that they operate very far from thermal equilibrium, in finite time, with many quickly (co-)evolving degrees of freedom. Such computers also must almost always obey multiple physical constraints on how they work. For example, all modern digital computers are periodic processes, governed by a global clock. Another example is that many computers are modular, hierarchical systems, with strong restrictions on the connectivity of their subsystems. This properties hold both for naturally occurring computers, like brains or Eukaryotic cells, as well as digital systems. These features of real-world computers are absent in 20th century analyses of the thermodynamics of computational processes, which focused on quasi-statically slow processes. However, the field of stochastic thermodynamics has been developed in the last few decades - and it provides the formal tools for analyzing systems that have exactly these features of real-world computers. We argue here that these tools, together with other tools currently being developed in stochastic thermodynamics, may help us understand at a far deeper level just how the fundamental physical properties of dynamic systems are related to the computation that they perform.

cond-mat.stat-mech↗

arXiv2024

On the area swept by a biased diffusion till its first-exit time: Martingale approach and gambling opportunities

Using martingale theory, we compute, in very few lines, exact analytical expressions for various first-exit-time statistics associated with one-dimensional biased diffusion. Examples include the distribution for the first-exit time from an interval, moments for the first-exit site, and functionals of the position, which involve memory and time integration. As a key example, we compute analytically the mean area swept by a biased diffusion until it escapes an interval that may be asymmetric and have arbitrary length. The mean area allows us to derive the hitherto unexplored cross-correlation function between the first-exit time and the first-exit site, which vanishes only for exit problems from symmetric intervals. As a colophon, we explore connections of our results with gambling, showing that betting on the time-integrated value of a losing game it is possible to design a strategy that leads to a net average win.

cond-mat.stat-mech↗

arXiv2024

Stochastic thermodynamics of a probe in a fluctuating correlated field

We develop a framework for the stochastic thermodynamics of a probe coupled to a fluctuating medium with spatio-temporal correlations, described by a scalar field. For a Brownian particle dragged by a harmonic trap through a fluctuating Gaussian field, we show that near criticality (where the field displays long-range spatial correlations) the spatially-resolved average heat flux develops a dipolar structure, where heat is absorbed in front and dissipated behind the dragged particle. Moreover, a perturbative calculation reveals that the dissipated power displays three distinct dynamical regimes depending on the drag velocity.

cond-mat.stat-mech↗