arXiv subjects

Aljaž Godec

Publications and source records attributed to Aljaž Godec.

At least 19 recordsLinked to original sources

arXiv2026

Thermodynamic Concentration Inequalities: Controlling Uncertainty in Finite-Time and Small-Sample Thermodynamic Inference

We derive nonasymptotic upper bounds on the probability that a generalized current of a geometrically ergodic diffusion observed for any amount of time, or its sample mean over any arbitrary sample size, deviates from the stationary mean by more than any given amount. The concentration-of-measure behavior of generalized currents is universally governed by the relaxation time of the underlying dynamics, the locally observed dissipation rate, and the intrinsic local fluctuations of the observable. We uncover stark qualitative and quantitative differences in fluctuations in and out of thermodynamic equilibrium. We further obtain refined inverse thermodynamic uncertainty relations, bounding the variance of generalized currents from above. We construct nonasymptotic confidence intervals for controlling uncertainty in thermodynamic inference from small data, i.e., from short trajectories and small samples, and provide the first quantitative answer to when a trajectory is sufficiently long and a sample is sufficiently large. As an illustration, we apply our results to currents observed on a two-dimensional Ornstein-Uhlenbeck process in and out of equilibrium and show how they can be used to rigorously detect broken detailed balance.

cond-mat.stat-mech↗

arXiv2026

Concentration of additive functionals of Stratonovich-type

Additive functionals $\overline{J}_t=\frac{1}{t}\int_0^tU(X_s)\circ dX_s$ of Stratonovich-type recently attracted much attention in the context of inference of thermodynamic properties of complex systems from observations $U$ of individual fluctuating paths $(X_s)_{0\le s\le t}$, whereby $X_0$ is initiated from some general measure. Concentration results on $\overline{J}_t$, albeit desirable, are virtually nonexistent. They turn out to be significantly more challenging to prove than for classical Lebesgue-type functionals $\overlineρ_t=\frac{1}{t}\int_0^t V(X_s)ds$ because the tilt deforms the full second-order structure of the Feynman-Kac generator instead of contributing an additive potential. This renders the generator generally non-self-adjoint even under detailed balance. We overcome this by working with a symmetrized Dirichlet form with a new effective potential that now couples the observable to the non-equilibrium character of the dynamics. We prove concentration inequalities for $\overline{J}_t$ for any bounded, sufficiently smooth vector-valued function $U$ of a general geometrically ergodic diffusion process $X_s$, including explicit sub-gamma and Bernstein-type inequalities, and we obtain explicit upper bounds on ${\rm Var}(\overline{J}_t)$. Strikingly, under detailed balance the concentration of $\overline{J}_t$ is distinctively sub-Gaussian at all times and all deviations, with a variance proxy fixed by the noise alone and independent of the spectral gap, which has no analog for $\overlineρ_t$.

math.PR↗

arXiv2026

Milestoning Markov-jump dynamics: Stationary properties, thermodynamic consistency, kinetic hysteresis, and fluctuation symmetries

We derive an exact coarse graining of generic Markov-jump processes into observable semi-Markov dynamics. Exact results for waiting-time distributions for jumps between observable states are derived and proved that these decompose into conditionally independent dwell and transition times. Dwell times are proved to be a local property of mesostates - they depend on the initial but not final state. Conversely, transition-path times depend on both states, trigger kinetic hysteresis, and, under suitable conditions on the hidden sub-network, are shown to obey a reflection symmetry. We characterize the stationary properties of the milestoned dynamics, prove its thermodynamic consistency, and demonstrate robustness to milestone positioning. Surprisingly, even in the limit of a time-scale separation rendering the observed dynamics approximately Markovian, the effect of kinetic hysteresis on the dissipation persists. A minimal example shows how the results lay the foundation for inferring affinities of hidden dissipative cycles from observations of transition-path times.

cond-mat.stat-mech↗

arXiv2026

Thermal relaxation asymmetry persists under inertial effects

Relaxation phenomena far from thermal equilibrium feature a rich phenomenology, including the thermal relaxation asymmetry. The latter states that heating occurs faster than cooling in overdamped harmonic systems quantified in terms of excess free energy and entropy production. Upon isolating the relevant relaxational contribution to the entropy production, we here algebraically prove the asymmetry in thermal relaxation in phase space in the entire range from overdamped dynamics to underdamped dynamics. We show that for the same setup as for overdamped dynamics (i.e., motion in harmonic potentials, also known as Ornstein-Uhlenbeck dynamics), even in the more general case of phase-space relaxation, i.e., underdamped dynamics, far-from-equilibrium heating is faster than cooling. The coupling of positions and velocities emerging in this generalization further underscores, in a striking manner, the intricate dynamics of such thermal relaxation processes that do not pass through local equilibria. Investigating the overdamped limit, our generalized approach reveals, interestingly, that an excess free energy contribution from the velocity degrees of freedom does not trivially vanish in the overdamped limit, but is instead affected by the precise interpretation of temperature quenches in overdamped systems.

cond-mat.stat-mech↗

arXiv2026

Permutation-Invariant Spectral Learning via Dyson Diffusion

Diffusion models are central to generative modeling and have been adapted to graphs by diffusing adjacency matrix representations. The challenge of having up to $n!$ such representations for graphs with $n$ nodes is only partially mitigated by using permutation-equivariant learning architectures. Despite their computational efficiency, existing graph diffusion models struggle to distinguish certain graph families and their spectra, unless graph data are augmented with ad hoc features. This shortcoming stems from enforcing the inductive bias within the learning architecture. In this work, we leverage random matrix theory to analytically extract the spectral properties of the diffusion process, allowing us to push most of the inductive bias from the architecture into the dynamics. Building on this, we introduce the Dyson Diffusion Model, which employs Dyson's Brownian motion to capture the spectral dynamics of an Ornstein-Uhlenbeck process on the adjacency matrix. Furthermore, conditioned on the spectral dynamics, we formulate a Lie group diffusion, appropriately modeling the remaining degrees of freedom. Strikingly, the resulting learning problem becomes permutation invariant at the Lie algebra level. We demonstrate that the Dyson Diffusion Model learns graph spectra accurately and outperforms existing graph diffusion models.

stat.ML↗

arXiv2026

Stochastic Calculus for Pathwise Observables of Markov-Jump Processes: Unification of Diffusion and Jump Dynamics

Path-wise observables--functionals of stochastic trajectories--are at the heart of time-average statistical mechanics and are central to thermodynamic inequalities such as uncertainty relations, speed limits, and correlation-bounds. They provide a means of thermodynamic inference in the typical situation, when not all dissipative degrees of freedom in a system are experimentally accessible. So far, theories focusing on path-wise observables have been developing in two major directions, diffusion processes and Markov-jump dynamics, in a virtually disjoint manner. Moreover, even the respective results for diffusion and jump dynamics were derived with a patchwork of different approaches that are predominantly indirect. Stochastic calculus was recently shown to provide a direct approach to path-wise observables of diffusion processes, while a corresponding framework for jump dynamics remained elusive. In our work we develop, in an exact parallelism with continuous-space diffusion, a complete stochastic calculus for path-wise observables of Markov-jump processes. We formulate a "Langevin equation" for jump processes, define general path-wise observables, and establish their covariation structure, whereby we fully account for transients and time-inhomogeneous dynamics. We prove the known kinds of thermodynamic inequalities in their most general form and discus saturation conditions. We determine the response of path-wise observables to general (incl. thermal) perturbations and introduce a corresponding response-function formalism. We carry out the continuum limit to achieve the complete unification of diffusion and jump dynamics. In addition, we connect the framework to quantum unraveling and the Belavkin equation for open quantum systems, associating quantum and classical descriptions of thermal systems.

cond-mat.stat-mech↗

arXiv2025

Consistent time reversal and reliable and accurate inference in the presence of memory

Thermodynamic inference from coarse observations remains a key challenge. Memory, in particular correlations between consecutively observed mesostates, blur signatures of irreversibility and must be accounted for in defining physical time-reversal, which remains an open problem. We derive an experimentally accessible k-th order estimator for the entropy production rate. Using novel measure-theoretic techniques we prove necessary and sufficient conditions for guaranteed lower bounds on the dissipation even in the strongly non-Markovian setting. The proof reveals that estimators saturated in the order unravel the duration of memory which needs to be considered in defining physically consistent time-reversal. We show that Markovian estimators in absence of a time-scale separation lead to artifacts, which convey no physical meaning. Similarly, estimators not saturated in the order may overestimate the dissipation. The necessity of correctly accounting for memory in thermodynamic inference from strongly non-Markovian observations underscores the still underappreciated challenges and intricacies in defining and understanding irreversibility in presence of memory. Our results will hopefully stimulate experiments systematically considering thermodynamic inference on multiple scales consistently accounting for memory.

cond-mat.stat-mech↗

arXiv2025

Purely quantum memory in closed systems observed via imperfect measurements

The detection and quantification of non-Markovianity, a.k.a. memory, in quantum systems is a central problem in the theory of open quantum systems. There memory is as a result of the interaction between the system and its environment. Little is known, however, about memory effects induced by imperfect measurements on closed systems, where an entanglement with the environment is not possible. We investigate the emergence and characteristics of memory in closed systems observed via imperfect stroboscopic quantum measurements yielding coarse-grained outcomes. We consider ideal and two kinds of imperfect measurements: von Neumann measurements--the analogue of classical lumping--which destroy any coherence in the system, and genuinely quantum-lumping Lüders measurements preserving certain quantum correlations. Whereas the conditions for Markov dynamics under von Neumann lumping are the same as for classical dynamics, quantum-lumping requires stronger conditions, i.e. the absence of any detectable coherence. We introduce the concept of purely quantum memory having no classical counterpart. We illustrate our results with a quantum walk on a lattice and discuss their implications for dissipative dynamics and decoherence effects induced by more realistic measurements.

quant-ph↗

arXiv2025

Towards Markov-State Holography

Experiments, in particular on biological systems, typically probe lower-dimensional observables which are projections of high-dimensional dynamics. In order to infer consistent models capturing the relevant dynamics of the system, it is important to detect and account for the memory in the dynamics. We develop a method to infer the presence of hidden states and transition pathways based on observable transition probabilities conditioned on history sequences of visited states for projected (i.e. observed) dynamics of Markov processes. Histograms conditioned on histories reveal information on the transition probabilities of hidden paths locally between any specific pair of observed states. The convergence rate of these histograms towards a stationary distribution provides a local quantification of the duration of memory, which reflects how distinct microscopic paths projecting onto the same observed transition decorrelate in path space. This motivates the notion of "weak Markov order" and provides insight about the hidden topology of microscopic paths in a holography-like fashion. The method can be used to test for the local Markov property of observables. The information extracted is also helpful in inferring relevant hidden transitions which are not captured by a Markov-state model.

cond-mat.stat-mech↗

arXiv2025

Dynamic Models for Two Nonreciprocally Coupled Fields: A Microscopic Derivation for Zero, One, and Two Conservation Laws

We construct dynamic models governing two nonreciprocally coupled fields for several cases with zero, one, and two conservation laws. Starting from two microscopic nonreciprocally coupled Ising models, and using the mean-field approximation, we obtain closed-form evolution equations for the spatially resolved magnetization in each lattice. Only allowing for single spin-flip dynamics, the macroscopic equations in the thermodynamic limit are closely related to the nonreciprocal Allen-Cahn equations, i.e. conservation laws are absent. Likewise, only accounting for spin-exchange dynamics within each lattice, the thermodynamic limit yields equations similar to the nonreciprocal Cahn-Hilliard model, i.e. with two conservation laws. In the case of spin-exchange dynamics within and between the two lattices, we obtain two nonreciprocally coupled equations that add up to one conservation law. For each of these cases, we systematically map out the linear instabilities that can arise. Moreover, combining the different dynamics gives a large number of further models. Our results provide a microscopic foundation for a broad class of nonreciprocal field theories, establishing a direct link between nonequilibrium statistical mechanics and macroscopic continuum descriptions.

cond-mat.stat-mech↗

arXiv2025

The allosteric lever: towards a principle of specific allosteric response

Allostery, the phenomenon by which the perturbation of a molecule at one site alters its behavior at a remote functional site, enables control over biomolecular function. Allosteric modulation is a promising avenue for drug discovery and is employed in the design of mechanical metamaterials. However, a general principle of allostery, i.e. a set of quantitative and transferable "ground rules", remains elusive. It is neither a set of structural motifs nor intrinsic motions. Focusing on elastic network models, we here show that an allosteric lever -- a mode-coupling pattern induced by the perturbation -- governs the directional, source-to-target, allosteric communication: a structural perturbation of an allosteric site couples the excitation of localized hard elastic modes with concerted long range soft-mode relaxation. Perturbations of non-allosteric sites instead couple hard and soft modes uniformly. The allosteric response is shown to be generally non-linear and non-reciprocal, and allows for minimal structural distortions to be efficiently transmitted to specific changes at distant sites. Allosteric levers exist in proteins and "pseudoproteins" -- networks designed to display an allosteric response. Interestingly, protein sequences that constitute allosteric transmission channels are shown to be evolutionarily conserved. To illustrate how the results may be applied in drug design, we use them to successfully predict known allosteric sites in proteins.

physics.bio-ph↗

arXiv2025

Thermodynamic correlation inequalities for finite times and transients

Recently, a thermodynamic bound on correlation times was formulated in [A. Dechant, J. Garnier-Brun, S.-i. Sasa, Phys. Rev. Lett. 131, 167101 (2023)], showing how the decay of correlations in Langevin dynamics is bounded by short-time fluctuations and dissipation. Whereas these original results only address very long observation times in steady-state dynamics, we here generalize the respective inequalities to finite observations and general initial conditions. We utilize the connection between correlations and the fluctuations of time-integrated density functionals and generalize the direct stochastic calculus approach from [C. Dieball and A. Godec, Phys. Rev. Lett. 130, 087101 (2023)] which paves the way for further generalizations. We address the connection between short and long time scales, as well as the saturation of the bounds via complementary spectral-theoretic arguments. Motivated by the spectral insight, we formulate all results also for complex-valued observables.

cond-mat.stat-mech↗

arXiv2025

Hallmarks of Deception in Asset-Exchange Models

We investigate the transient and steady-state dynamics of the Bennati-Dragulescu-Yakovenko money game in the presence of probabilistic cheaters, who can misrepresent their financial status by claiming to have no money. We derive the steady-state wealth distribution per player analytically, and show how the presence of hidden cheaters can be inferred from the relative variance of wealth per player. In scenarios with a finite number of cheaters amidst an infinite pool of honest players, we identify a critical probability of cheating at which the total wealth owned by the cheaters experiences a second-order discontinuity. Below this point, the transition probability to lose money is larger than the probability to gain; conversely, above this point, the direction is reversed. We further establish a threshold cheating probability at which cheaters collectively possess half of the total wealth in the game. Lastly, we provide bounds on the rate at which both cheaters and honest players can gain or lose wealth, contributing to a deeper understanding of deception in asset exchange models.

cond-mat.stat-mech↗

arXiv2025

Precisely controlled colloids: A playground for path-wise non-equilibrium physics

We investigate path-wise observables in experiments on driven colloids in a periodic light field to dissect selected intricate transport features, kinetics, and transition-path time statistics out of thermodynamic equilibrium. These observables directly reflect the properties of individual paths in contrast to the properties of an ensemble of particles, such as radial distribution functions or mean-squared displacements. In particular, we present two distinct albeit equivalent formulations of the underlying stochastic equation of motion, highlight their respective practical relevance, and show how to interchange between them. We discuss conceptually different notions of local velocities and interrogate one- and two-sided first-passage and transition-path time statistics in and out of equilibrium. Our results reiterate how path-wise observables may be employed to systematically assess the quality of experimental data and demonstrate that, given sufficient control and sampling, one may quantitatively verify subtle theoretical predictions.

cond-mat.soft↗

arXiv2025

Perspective: Time irreversibility in systems observed at coarse resolution

A broken time-reversal symmetry, i.e. broken detailed balance, is central to non-equilibrium physics and is a prerequisite for life. However, it turns out to be quite challenging to unambiguously define and quantify time-reversal symmetry (and violations thereof) in practice, that is, from observations. Measurements on complex systems have a finite resolution and generally probe low-dimensional projections of the underlying dynamics, which are well known to introduce memory. In situations where many microscopic states become "lumped" onto the same observable "state" or when introducing "reaction coordinates" to reduce the dimensionality of data, signatures of a broken time-reversal symmetry in the microscopic dynamics become distorted or masked. In this perspective we highlight why in defining and discussing time-reversal symmetry, and quantifying its violations, the precise underlying assumptions on the microscopic dynamics, the coarse graining, and further reductions, are not a technical detail. These assumptions decide whether the conclusions that are drawn are physically sound or inconsistent. We summarize recent findings in the field and reflect upon key challenges.

cond-mat.stat-mech↗

arXiv2025

Local Order Controls the Onset of Oscillations in the Nonreciprocal Ising Model

We elucidate the generic bifurcation behavior of local and global order in the nonreciprocal Ising model evolving under Glauber dynamics. We show that a critical magnitude of nearest-neighbor correlations within the respective lattices controls the emergence of coherent oscillations of global order as a result of frustration. Local order is maintained during these oscillations, implying nontrivial spatiotemporal correlations. Long-lived states emerge in the strong-interaction regime. The residence time in either of these states eventually diverges, giving rise to ordered non-equilibrium trapped states and a loss of ergodic behavior via a saddle-node-infinite-period bifurcation. Our work provides a comprehensive microscopic understanding of the nonreciprocal Ising model beyond the mean-field approximation.

cond-mat.stat-mech↗

arXiv2024

Multiple Pareto-optimal solutions of the dissipation-adaptation trade-off

Adaptation refers to the ability to recover and maintain ``normal'' function upon perturbations of internal or external conditions and is essential for sustaining life. Biological adaptation mechanisms are dissipative, i.e. they require a supply of energy such as the coupling to the hydrolysis of ATP. Via evolution the underlying biochemical machinery of living organisms evolved into highly optimized states. However, in the case of adaptation processes two quantities are optimized simultaneously, the adaptation speed or accuracy and the thermodynamic cost. In such cases one typically faces a trade-off, where improving one quantity implies worsening the other. The solution is no longer unique but rather a Pareto set -- the set of all physically attainable protocols along which no quantity can be improved without worsening another. Here we investigate Pareto fronts in adaptation-dissipation trade-offs for a cellular thermostat and a minimal ATP-driven receptor-ligand reaction network. We find convex sections of Pareto fronts to be interrupted by concave regions, implying the coexistence of distinct optimization mechanisms. We discuss the implications of such ``compromise-optimal'' solutions and argue that they may endow biological systems with a superior flexibility to evolve, resist, and adapt to different environments.

cond-mat.stat-mech↗

arXiv2024

Thermodynamic bounds on generalized transport: From single-molecule to bulk observables

We prove that the transport of any differentiable scalar observable in $d$-dimensional non-equilibrium systems is bounded from above by the total entropy production scaled by the amount the observation "stretches" microscopic coordinates. The result--a time-integrated generalized speed limit--reflects the thermodynamic cost of transport of observables, and places underdamped and overdamped stochastic dynamics on equal footing with deterministic motion. Our work allows for stochastic thermodynamics to make contact with bulk experiments, and fills an important gap in thermodynamic inference, since microscopic dynamics is, at least for short times, underdamped. Requiring only averages but not sample-to-sample fluctuations, the proven transport bound is practical and applicable not only to single-molecule but also bulk experiments where only averages are observed, which we demonstrate by examples. Our results may facilitate thermodynamic inference on molecular machines without an obvious directionality from bulk observations of transients probed, e.g.\ in time-resolved X-ray scattering.

cond-mat.stat-mech↗