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In this work, we investigate the multimode Brownian oscillators in nonequilibrium scenarios with multiple reservoirs at different temperatures. For this purpose, an algebraic method is proposed. This approach gives the exact time-local equation of motion for the reduced density operator, from which we can easily extract not only the reduced system but also hybrid bath dynamical information. The resulting steady-state heat current is found to be numerically consistent with another discrete imaginary-frequency method followed by Meir–Wingreen's formula. It is anticipated that the development in this work would constitute an indispensable component of nonequilibrium statistical mechanics for open quantum systems. [ABSTRACT FROM AUTHOR]

Flow fluctuations that are commonly associated with multiphase flow in porous media are studied using concepts from non-equilibrium thermodynamic and statistical mechanics. We investigate how the Green–Kubo formulation of the fluctuation dissipation theorem can be used to predict the transport coefficient from the two-phase extension of Darcy's law. Flow rate-time series data are recorded at the millisecond timescale using a novel experimental setup that allows for the determination of flow fluctuation statistics. By using Green–Kubo relations, a transport coefficient is predicted based on the integrated autocorrelation function. Notably, this coefficient aligned closely with the total effective phase mobility computed using Darcy's equation for multiphase flow, particularly in scenarios where a linear relationship between flow rate and pressure gradient was observed. Our results open a new field of coefficient explorations where microscale fluctuations during multiphase flow are directly linked to macroscale parameters. [ABSTRACT FROM AUTHOR]

Energy transfer in small nano-sized systems can be very different from that in their macroscopic counterparts due to reduced dimensionality, interaction with surfaces, disorder, and large fluctuations. Those ingredients may induce non-diffusive heat transfer that requires to be taken into account on small scales. We provide an overview of the recent advances in this field from the points of view of nonequilibrium statistical mechanics and atomistic simulations. We summarize the underlying basic properties leading to violations of the standard diffusive picture of heat transport and its universal features, with some historical perspective. We complete this scenario by illustrating also the effects of long-range interaction and integrability on non-diffusive transport. Then we discuss how all of these features can be exploited for thermal management, rectification and to improve the efficiency of energy conversion. We conclude with a review on recent achievements in atomistic simulations of anomalous heat transport in single polymers, nanotubes and two-dimensional materials. A short account of the existing experimental literature is also given.

The adaptive and surprising emergent properties of biological materials self-assembled in far-from-equilibrium environments serve as an inspiration for efforts to design nanomaterials. In particular, controlling the conditions of self-assembly can modulate material properties, but there is no systematic understanding of either how to parameterize external control or how controllable a given material can be. Here, we demonstrate that branched actin networks can be encoded with metamaterial properties by dynamically controlling the applied force under which they grow and that the protocols can be selected using multi-task reinforcement learning. These actin networks have tunable responses over a large dynamic range depending on the chosen external protocol, providing a pathway to encoding "memory" within these structures. Interestingly, we obtain a bound that relates the dissipation rate and the rate of "encoding" that gives insight into the constraints on control--both physical and information theoretical. Taken together, these results emphasize the utility and necessity of nonequilibrium control for designing self-assembled nanostructures. [ABSTRACT FROM AUTHOR]

Using the property of the solution of the Langevin dynamics with a generalized frictional memory kernel and time-dependent deterministic force field, we show that a solution method (which is very simple as well as shortcut) can be used to derive a Fokker–Planck-like equation (FPLE) for this dynamics. Using this equation, we derive a relation to find the modulation of the entropy production by a time-dependent external force. We also derive a relation between the phase space dynamics and the entropy production of the irreversible thermodynamics. Then FPLE is derived for the non-Markovian dynamics with additional force from harmonic potential, magnetic fields, and both, respectively. Thus, the method is instructive in deriving the FPLE in a shortcut way in the presence of an additional time-dependent stochastic force. Here, we have to consider that the relevant drift terms are independent of the random force.Another very important point is to be noted here. To interpret the FPLE, we recognize that the memory of the non-Markovian dynamics can induce a fictitious electric field in the presence of a time-independent magnetic field and a conservative force field. Then one may notice that like the forces from time-dependent damping, harmonic potential and magnetic field, the fictitious electric field puts its own identity to modulate the effect of the external time-dependent force in the presence of a non-Markovian thermal bath. We understand that the present study, with the recognition of the induced electric field-like quantity, will bring strong attention to different areas of non-equilibrium statistical mechanics, such as the physical tuning of conductivity of ions in solid electrolytes and stochastic thermodynamics for non-Markovian dynamics, etc. [ABSTRACT FROM AUTHOR]

Living systems use chemical fuel to process information, assemble structures and maintain fluxes. Many of these processes are dissipative: energy is consumed purely to maintain non-equilibrium steady-state outputs. How efficiently the input energy is transduced toward the output dissipation as opposed to being lost during intermediate steps, and whether the efficiency is constrained by general principles or specific fine-tuning, are open questions. Here, applying a recent mapping from non-equilibrium systems to battery-resistor circuits, an analytic expression for the efficiency of any dissipative molecular machine driven by a chemical potential difference is derived. This expression disentangles the chemical potential from the machine's details, whose effect on the efficiency is fully specified by a constant called the load resistance. The efficiency passes through an inflection point separating totally inefficient machines from efficient machines if the balance between chemical potential difference and load resistance exceeds thermal noise. This explains all-or-none dynein stepping with increasing ATP concentration observed in single-molecule experiments. These results indicate that energy transduction in living systems is efficient not because of idiosyncratic optimization of biomolecules, but rather because the concentration of chemical fuel is kept above a threshold level. [ABSTRACT FROM AUTHOR]

Only a subset of degrees of freedom are typically accessible or measurable in real-world systems. As a consequence, the proper setting for empirical modeling is that of partially-observed systems. Notably, data-driven models consistently outperform physics-based models for systems with few observable degrees of freedom; e.g. hydrological systems. Here, we provide an operator-theoretic explanation for this empirical success. To predict a partially-observed system’s future behavior with physics-based models, the missing degrees of freedom must be explicitly accounted for using data assimilation and model parametrization. Data-driven models, in contrast, employ delay-coordinate embeddings and their evolution under the Koopman operator to implicitly model the effects of the missing degrees of freedom. We describe in detail the statistical physics of partial observations underlying data-driven models using novel maximum entropy and maximum caliber measures. The resulting nonequilibrium Wiener projections applied to the Mori-Zwanzig formalism reveal how data-driven models may converge to the true dynamics of the observable degrees of freedom. Additionally, this framework shows how data-driven models infer the effects of unobserved degrees of freedom implicitly, in much the same way that physics models infer the effects explicitly. This provides a unified implicit-explicit modeling framework for predicting partially-observed systems, with hybrid physics-informed machine learning methods combining both implicit and explicit aspects.

When a physical system is driven away from equilibrium, the statistical distribution of its dynamical trajectories informs many of its physical properties. Characterizing the nature of the distribution of dynamical observables, such as a current or entropy production rate, has become a central problem in nonequilibrium statistical mechanics. Asymptotically, for a broad class of observables, the distribution of a given observable satisfies a large deviation principle when the dynamics is Markovian, meaning that fluctuations can be characterized in the long-time limit by computing a scaled cumulant generating function. Calculating this function is not tractable analytically (nor often numerically) for complex, interacting systems, so the development of robust numerical techniques to carry out this computation is needed to probe the properties of nonequilibrium materials. Here, we describe an algorithm that recasts this task as an optimal control problem that can be solved variationally. We solve for optimal control forces using neural network ansatz that are tailored to the physical systems to which the forces are applied. We demonstrate that this approach leads to transferable and accurate solutions in two systems featuring large numbers of interacting particles. [ABSTRACT FROM AUTHOR]

In this article, we address the reliance on probability density functions to obtain macroscopic properties in systems with multiple degrees of freedom as plasmas, and the limitations of expensive techniques for solving Equations such as Vlasov's. We introduce the Ehrenfest procedure as an alternative tool that promises to address these challenges more efficiently. Based on the conjugate variable theorem and the well-known fluctuation-dissipation theorem, this procedure offers a less expensive way of deriving time evolution Equations for macroscopic properties in systems far from equilibrium. We investigate the application of the Ehrenfest procedure for the study of adiabatic invariants in magnetized plasmas. We consider charged particles trapped in a dipole magnetic field and apply the procedure to the study of adiabatic invariants in magnetized plasmas and derive Equations for the magnetic moment, longitudinal invariant, and magnetic flux. We validate our theoretical predictions using a test particle simulation, showing good agreement between theory and numerical results for these observables. Although we observed small differences due to time scales and simulation limitations, our research supports the utility of the Ehrenfest procedure for understanding and modeling the behavior of particles in magnetized plasmas. We conclude that this procedure provides a powerful tool for the study of dynamical systems and statistical mechanics out of equilibrium, and opens perspectives for applications in other systems with probabilistic continuity. [ABSTRACT FROM AUTHOR]

Large deviations of conservative interacting particle systems, such as the zero range process, about their hydrodynamic limit and their respective rate functions lead to the analysis of the skeleton equation; a degenerate parabolic-hyperbolic PDE with irregular drift. We develop a robust well-posedness theory for such PDEs in energy-critical spaces based on concepts of renormalized solutions and the equation's kinetic form. We establish these properties by proving that renormalized solutions are equivalent to classical weak solutions, extending concepts of (DiPerna and Lions in Invent. Math. 98(3):511–547, 1989; Ambrosio in Invent. Math. 158(2):227–260, 2004) to the nonlinear setting. The relevance of the results toward large deviations in interacting particle systems is demonstrated by applications to the identification of l.s.c. envelopes of restricted rate functions, to zero noise large deviations for conservative SPDE, and to the Γ -convergence of rate functions. The first of these solves a long-standing open problem in the large deviations for zero range processes. The second makes rigorous an informal link between the non-equilibrium statistical mechanics approaches of macroscopic fluctuation theory and fluctuating hydrodynamics. [ABSTRACT FROM AUTHOR]

Resetting a stochastic process is an important problem describing the evolution of physical, biological and other systems which are continually returned to their certain fixed point. We consider the motion of a subdiffusive particle with a constant drift under Poissonian resetting. In this model the stochastic process is Brownian motion subordinated by an inverse infinitely divisible process (subordinator). Although this approach includes a wide class of subdiffusive system with Poissonian resetting by using different subordinators, each of such systems has a stationary state with the asymmetric Laplace distribution in which the scale and asymmetric parameters depend on the Laplace exponent of the subordinators used. Moreover, the mean time for the particle to reach a target is finite and has a minimum, optimal with respect to the resetting rate. Features of Lévy motion under this resetting and the effect of a linear potential are discussed. [ABSTRACT FROM AUTHOR]

The last decade has witnessed a surge of theoretical and computational models to describe the dynamics of complex gene regulatory networks, and how these interactions can give rise to multistable and heterogeneous cell populations. As the use of theoretical modeling to describe genetic and biochemical circuits becomes more widespread, theoreticians with mathematical and physical backgrounds routinely apply concepts from statistical physics, non-linear dynamics, and network theory to biological systems. This review aims at providing a clear overview of the most important methodologies applied in the field while highlighting current and future challenges. It also includes hands-on tutorials to solve and simulate some of the archetypical biological system models used in the field. Furthermore, we provide concrete examples from the existing literature for theoreticians that wish to explore this fast-developing field. Whenever possible, we highlight the similarities and differences between biochemical and regulatory networks and 'classical' systems typically studied in non-equilibrium statistical and quantum mechanics. [ABSTRACT FROM AUTHOR]

Any real physical process that produces entropy, dissipates energy as heat, or generates mechanical work must do so on a finite timescale. Recently derived thermodynamic speed limits place bounds on these observables using intrinsic timescales of the process. Here, we derive relationships for the thermodynamic speeds for any composite stochastic observable in terms of the timescales of its individual components. From these speed limits, we find bounds on thermal efficiency of stochastic processes exchanging energy as heat and work and bound the rate of entropy change in a system with entropy production and flow. Using the time set by an external clock, we find bounds on the first time to reach any value for the entropy production. As an illustration, we compute these bounds for Brownian particles diffusing in space subject to a constant-temperature heat bath and a time-dependent external force. [ABSTRACT FROM AUTHOR]

We derive a bound for entropy production in terms of the mean of normalizable path-antisymmetric observables. The optimal observable for this bound is shown to be the signum of entropy production, which is often easier determined or estimated than entropy production itself. It can be preserved under coarse graining by the use of a simple path grouping algorithm. We demonstrate this relation and its properties using a driven network on a ring, for which the bound saturates for short times for any driving strength. This work can open a way to systematic coarse graining of entropy production. [ABSTRACT FROM AUTHOR]

Nonequilibrium statistical mechanics exhibit a variety of complex phenomena far from equilibrium. It inherits challenges of equilibrium, including accurately describing the joint distribution of a large number of configurations, and also poses new challenges as the distribution evolves over time. Characterizing dynamical phase transitions as an emergent behavior further requires tracking nonequilibrium systems under a control parameter. While a number of methods have been proposed, such as tensor networks for one-dimensional lattices, we lack a method for arbitrary time beyond the steady state and for higher dimensions. Here, we develop a general computational framework to study the time evolution of nonequilibrium systems in statistical mechanics by leveraging variational autoregressive networks, which offer an efficient computation on the dynamical partition function, a central quantity for discovering the phase transition. We apply the approach to prototype models of nonequilibrium statistical mechanics, including the kinetically constrained models of structural glasses up to three dimensions. The approach uncovers the active-inactive phase transition of spin flips, the dynamical phase diagram, as well as new scaling relations. The result highlights the potential of machine learning dynamical phase transitions in nonequilibrium systems. Variational autoregressive networks have been employed in the study of equilibrium statistical mechanics, chemical reaction networks and quantum many-body systems. Using these tools, Tang et al. develop a general approach to nonequilibrium statistical mechanics problems, such as dynamical phase transitions. [ABSTRACT FROM AUTHOR]

Jiang and co-workers use high resolution STM to investigate the reaction and self-assembly of (3,6-dibromo-9,10-phenanthrenequinone, or DBPQ) molecules on Ag (100) and Ag (110) surfaces in order to understand the mechanism of bottom-up assembly on surfaces.[31] They show that, through the inclusion of multiple functional groups within a precursor molecule, it becomes possible to fabricate new low-dimensional materials with unique chemical, physical, and electronic properties. Herbst and Fransson consider the core-valence separation approximation that is often used in the calculation of core-level spectra.[5] They show how to quantify the errors in this approximation, thereby opening the door to error-quantified predictions relevant to x-ray spectroscopy. 153(16), 164108 (2020).10.1063/5.0019557 5 M. F. Herbst and T. Fransson, "Quantifying the error of the core-valence separation approximation", J. Chem. Phys. Zhu and co-workers tackle this problem for a model system containing a 2D semiconductor heterojunction and show convincingly the efficient hot electron transfer from photoexcited MoTe SB 2 sb to WS SB 2 sb .[30] This finding provides important insight into the competition between hot electron cooling and transfer at 2D semiconductor interfaces and suggests an intriguing possibility for the exploration of hot electron devices. [Extracted from the article]

This article provides information about the upcoming third Japan-U.S. symposium on motor proteins and associated single-molecule biophysics, which will be held during the 61st Annual Meeting of the Biophysical Society of Japan. The symposium aims to facilitate collaboration and idea exchange between researchers from Japan and the U.S. in the field of microtubule-based motility and motor proteins. It will cover topics such as the molecular properties and regulatory mechanisms of motors, their role in plant cells, and the transport of alphaherpesviruses in neurons. The symposium will feature talks by experts in the field and is expected to advance research in this area. Additionally, the document includes a list of scientific articles related to microtubule-based transport and organelle movement within cells, covering various aspects of the field including protein identification, vortex lattices, competition between motor proteins, and the role of kinesins and dyneins in intracellular transport, neurological disorders, and viral infection. These references are valuable resources for researchers studying these topics. [Extracted from the article]

In this work, we proposed a smooth transition wave equation from a quantum to classical regime in the framework of von Neumann formalism for ensembles and then obtained an equivalent scaled equation. This led us to develop a scaled statistical theory following the well-known Wigner–Moyal approach of quantum mechanics. This scaled nonequilibrium statistical mechanics has in it all the ingredients of the classical and quantum theory described in terms of a continuous parameter displaying all the dynamical regimes in between the two extreme cases. Finally, a simple application of our scaled formalism consisting of reflection from a mirror by computing various quantities, including probability density plots, scaled trajectories, and arrival times, was analyzed. [ABSTRACT FROM AUTHOR]

Resetting a stochastic process is an important problem describing the evolution of physical, biological and other systems which are continually returned to their certain fixed point. We consider the motion of a subdiffusive particle with a constant drift under Poissonian resetting. In this model the stochastic process is Brownian motion subordinated by an inverse infinitely divisible process (subordinator). Although this approach includes a wide class of subdiffusive system with Poissonian resetting by using different subordinators, each of such systems has a stationary state with the asymmetric Laplace distribution in which the scale and asymmetric parameters depend on the Laplace exponent of the subordinators used. Moreover, the mean time for the particle to reach a target is finite and has a minimum, optimal with respect to the resetting rate. Features of Lévy motion under this resetting and the effect of a linear potential are discussed.

We are describing relations between Schrödinger's variational problem and Onsager's approach to nonequilibrium statistical mechanics. Although the second work on reciprocal relations and detailed balance has been published in the same year (1931) as the first one, the impact of Schrödinger's idea has not yet been considered in the classical context of Onsager. [ABSTRACT FROM AUTHOR]

Multi-component molecular machines are ubiquitous in biology. We review recent progress on describing their thermodynamic properties using autonomous bipartite Markovian dynamics. The first and second laws can be split into separate versions applicable to each subsystem of a two-component system, illustrating that one can not only resolve energy flows between the subsystems but also information flows quantifying how each subsystem’s dynamics influence the joint system’s entropy balance. Applying the framework to molecular-scale sensors allows one to derive tighter bounds on their energy requirement. Two-component strongly coupled machines can be studied from a unifying perspective quantifying to what extent they operate conventionally by transducing power or like an information engine by generating information flow to rectify thermal fluctuations into output power.

Understanding how hydrodynamic behaviour emerges from the unitary evolution of the many-particle Schrödinger equation is a central goal of non-equilibrium statistical mechanics. In this work we implement a digital simulation of the discrete time quantum dynamics of a spin- 1 2 XXZ spin chain on a noisy near-term quantum device, and we extract the high temperature transport exponent at the isotropic point. We simulate the temporal decay of the relevant spin correlation function at high temperature using a pseudo-random state generated by a random circuit that is specifically tailored to the ibmq-montreal 27 qubit device. The resulting output is a spin excitation on a homogeneous background on a 21 qubit chain on the device. From the subsequent discrete time dynamics on the device we are able to extract an anomalous super-diffusive exponent consistent with the conjectured Kardar-Parisi-Zhang (KPZ) scaling at the isotropic point. Furthermore we simulate the restoration of spin diffusion with the application of an integrability breaking potential. [ABSTRACT FROM AUTHOR]

Organisms often use cyclic changes in the concentrations of chemical species to precisely time biological functions. Underlying these biochemical clocks are chemical reactions and transport processes, which are inherently stochastic. Understanding the physical basis for robust biochemical oscillations in the presence of fluctuations has thus emerged as an important problem. In a previous paper [C. del Junco and S. Vaikuntanathan, Phys. Rev. E 101, 012410 (2020)], we explored this question using the non-equilibrium statistical mechanics of single-ring Markov state models of biochemical networks that support oscillations. Our finding was that they can exploit non-equilibrium driving to robustly maintain the period and coherence of oscillations in the presence of randomness in the rates. Here, we extend our work to Markov state models consisting of a large cycle decorated with multiple small cycles. These additional cycles are intended to represent alternate pathways that the oscillator may take as it fluctuates about its average path. Combining a mapping to single-cycle networks based on first passage time distributions with our previously developed theory, we are able to make analytical predictions for the period and coherence of oscillations in these networks. One implication of our predictions is that a high energy budget can make different network topologies and arrangements of rates degenerate as far as the period and coherence of oscillations are concerned. Excellent agreement between analytical and numerical results confirms that this is the case. Our results suggest that biochemical oscillators can be more robust to fluctuations in the path of the oscillator when they have a high energy budget. [ABSTRACT FROM AUTHOR]

Complex systems can exhibit sudden transitions or regime shifts from one stable state to another, typically referred to as critical transitions. It becomes a great challenge to identify a robust warning sufficiently early that action can be taken to avert a regime shift. We employ landscape-flux theory from nonequilibrium statistical mechanics as a general framework to quantify the global stability of ecological systems and provide warning signals for critical transitions. We quantify the average flux as the nonequilibrium driving force and the dynamical origin of the nonequilibrium transition while the entropy production rate as the nonequilibrium thermodynamic cost and thermodynamic origin of the nonequilibrium transition. Average flux, entropy production, nonequilibrium free energy, and time irreversibility quantified by the difference in cross-correlation functions forward and backward in time can serve as early warning signals for critical transitions much earlier than other conventional predictors. We utilize a classical shallow lake model as an exemplar for our early warning prediction. Our proposed method is general and can be readily applied to assess the resilience of many other ecological systems. The early warning signals proposed here can potentially predict critical transitions earlier than established methods and perhaps even sufficiently early to avert catastrophic shifts. [ABSTRACT FROM AUTHOR]

We discuss the possibility of defining an emergent local temperature in extended quantum many-body systems evolving out of equilibrium. For the most simple case of free-fermionic systems, we give an explicit formula for the effective temperature in the case of, not necessarily unitary, Gaussian preserving dynamics. In this framework, we consider the hopping fermions on a one-dimensional lattice submitted to randomly distributed projective measurements of the local occupation numbers. We show from the average over many quantum trajectories that the effective temperature relaxes exponentially towards infinity. [ABSTRACT FROM AUTHOR]

When a physical system evolves in a thermal bath kept at a constant temperature, it eventually reaches an equilibrium state which properties are independent of the kinetic parameters and of the precise evolution scenario. This is generically not the case for a system driven out of equilibrium which, on the contrary, reaches a steady-state with properties that depend on the full details of the dynamics such as the driving noise and the energy dissipation. How the steady state depends on such parameters is in general a non-trivial question. Here, we approach this broad problem using a minimal model of a two-dimensional nano-machine, the Brownian gyrator, that consists of a trapped particle driven by fractional Gaussian noises—a family of noises with long-ranged correlations in time and characterized by an anomalous diffusion exponent α. When the noise is different in the different spatial directions, our fractional Brownian gyrator persistently rotates. Even if the noise is non-trivial, with long-ranged time correlations, thanks to its Gaussian nature we are able to characterize analytically the resulting nonequilibrium steady state by computing the probability density function, the probability current, its curl and the angular velocity and complement our study by numerical results. [ABSTRACT FROM AUTHOR]

In this paper, we proceed to illustrate the consequences and implications of the Dual Model of Liquids (DML) by applying it to the heat propagation. Within the frame of the DML, propagation of thermal (elastic) energy in liquids is due to wave-packet propagation and to the wave-packets' interaction with the material particles of the liquid, meant in the DML as aggregates of molecules swimming in an ocean of amorphous liquid. The liquid particles interact with the lattice particles, a population of elastic wave-packets, by means of an inertial force, exchanging energy and momentum with them. The hit particle relaxes at the end of the interaction, releasing the energy and momentum back to the system a step forward and a time lapse later, like in a tunnel effect. The tunnel effect and the duality of liquids are the new elements that suggest on a physical basis for the first time, using a hyperbolic equation to describe the propagation of energy associated to the dynamics of wave-packet interaction with liquid particles. Although quantitatively relevant only in the transient phase, the additional term characterizing the hyperbolic equation, usually named the "memory term", is physically present also once the stationary state is attained; it is responsible for dissipation in liquids and provides a finite propagation velocity for wave-packet avalanches responsible in the DML for the heat conduction. The consequences of this physical interpretation of the "memory" term added to the Fourier law for the phononic contribution are discussed and compiled with numerical prediction for the value of the memory term and with the conclusions of other works on the same topic. [ABSTRACT FROM AUTHOR]

An overview is presented of several diverse branches of work in the area of effectively 2D fluid equilibria which have in common that they are constrained by an infinite number of conservation laws. Broad concepts, and the enormous variety of physical phenomena that can be explored, are highlighted. These span, roughly in order of increasing complexity, Euler flow, nonlinear Rossby waves, 3D axisymmetric flow, shallow water dynamics, and 2D magnetohydrodynamics. The classical field theories describing these systems bear some resemblance to perhaps more familiar fluctuating membrane and continuous spin models, but the fluid physics drives these models into unconventional regimes exhibiting large scale jet and eddy structures. From a dynamical point of view these structures are the end result of various conserved variable forward and inverse cascades. The resulting balance between large scale structure and small scale fluctuations is controlled by the competition between energy and entropy in the system free energy, in turn highly tunable through setting the values of the conserved integrals. Although the statistical mechanical description of such systems is fully self-consistent, with remarkable mathematical structure and diversity of solutions, great care must be taken because the underlying assumptions, especially ergodicity, can be violated or at minimum lead to exceedingly long equilibration times. Generalization of the theory to include weak driving and dissipation (e.g., non-equilibrium statistical mechanics and associated linear response formalism) could provide additional insights, but has yet to be properly explored. [ABSTRACT FROM AUTHOR]

We study a class of dynamical semigroups ( n) n ∈ ℕ that emerge, by a Feynman–Kac type formalism, from a random quantum dynamical system (ℒ ω n ∘ ⋯ ∘ ℒ ω 1 (ρ ω 0 )) n ∈ ℕ driven by a Markov chain (ω n) n ∈ ℕ . We show that the almost sure large time behavior of the system can be extracted from the large n asymptotics of the semigroup, which is in turn directly related to the spectral properties of the generator . As a physical application, we consider the case where the ℒ ω 's are the reduced dynamical maps describing the repeated interactions of a system with thermal probes ℰ ω . We study the full statistics of the entropy in this system and derive a fluctuation theorem for the heat exchanges and the associated linear response formulas. [ABSTRACT FROM AUTHOR]

We discuss the possibility of defining an emergent local temperature in extended quantum many-body systems evolving out of equilibrium. For the most simple case of free-fermionic systems, we give an explicit formula for the effective temperature in the case of, not necessarily unitary, Gaussian preserving dynamics. In this framework, we consider the hopping fermions on a one-dimensional lattice submitted to randomly distributed projective measurements of the local occupation numbers. We show from the average over many quantum trajectories that the effective temperature relaxes exponentially towards infinity.

In this paper, we show that ensembles of well-structured and unstructured proteins can be distinguished by borrowing concepts from non-equilibrium statistical mechanics. For this purpose, we represent proteins by two different polymer models and interpret the resulting polymer configurations as random walks of a diffusing particle in space. The first model is the trace of the Cα-atoms along the protein main chain, and the second is their projections onto the protein axis. The resulting trajectories are subsequently analyzed using the theory of the generalized Langevin equation. Velocities are replaced by displacements relating consecutive points on the discrete protein axes and equilibrium ensemble averages by averages over appropriate protein structure ensembles. The resulting displacement autocorrelation functions resemble those of the velocity autocorrelation functions of simple liquids and display a minimum, which can be related to the lengths of secondary structure elements. This minimum is clearly more pronounced for well-structured proteins than for unstructured ones, and the corresponding memory function displays a slower decay, indicating a stronger "folding memory." [ABSTRACT FROM AUTHOR]

We analyze structure-preserving model order reduction methods for Ornstein–Uhlenbeck processes and linear S(P)DEs with multiplicative noise based on balanced truncation. For the first time, we include in this study the analysis of non-zero initial conditions. We moreover allow for feedback-controlled dynamics for solving stochastic optimal control problems with reduced-order models and prove novel error bounds for a class of linear quadratic regulator problems. We provide numerical evidence for the bounds and discuss the application of our approach to enhanced sampling methods from non-equilibrium statistical mechanics. [ABSTRACT FROM AUTHOR]

Ecological and evolutionary processes show various population dynamics depending on internal interactions and environmental changes. While crucial in predicting biological processes, discovering general relations for such nonlinear dynamics has remained a challenge. Here, we derive a universal information-theoretical constraint on a broad class of nonlinear dynamical systems represented as population dynamics. The constraint is interpreted as a generalization of Fisher's fundamental theorem of natural selection. Furthermore, the constraint indicates nontrivial bounds for the speed of critical relaxation around bifurcation points, which we argue are universally determined only by the type of bifurcation. Our theory is verified for an evolutionary model and an epidemiological model, which exhibit the transcritical bifurcation, as well as for an ecological model, which undergoes limit-cycle oscillation. This work paves a way to predict biological dynamics in light of information theory, by providing fundamental relations in nonequilibrium statistical mechanics of nonlinear systems. In evolutionary theory, Fisher's fundamental theorem of natural selection establishes a simple relation between the variance of the growth rate and the temporal increase in the average growth rate. Here, the authors extend the theorem based on statistical physics and information theory and show that the speed in dynamical systems describing nonlinear population dynamics is bounded by Fisher information with universal limit exponents only depending on the kind of bifurcation and not on the specific systems. [ABSTRACT FROM AUTHOR]

mRNA translation is a crucial process that leads to protein synthesis in living cells. Therefore, it is a process that needs to work optimally for a cell to stay healthy and alive. With advancements in microscopy and novel experimental techniques, a lot of the intricate details about the translation mechanism are now known. However, the why and how of this mechanism are still ill understood, and therefore, is an active area of research. Theoretical studies of mRNA translation typically view it in terms of the Totally Asymmetric Simple Exclusion Process or TASEP. Various works have used the TASEP model in order to study a wide range of phenomena and factors affecting translation, such as ribosome traffic on an mRNA under noisy (codon-dependent or otherwise) conditions, ribosome stalling, premature termination, ribosome reinitiation and dropoff, codon-dependent elongation and competition among mRNA for ribosomes, among others. In this review, we relay the history and physics of the translation process in terms of the TASEP framework. In particular, we discuss the viability and evolution of this model and its limitations while also formulating the reasons behind its success. Finally, we also identify gaps in the existing literature and suggest possible extensions and applications that will lead to a better understanding of ribosome traffic on the mRNA. • Explanation of the translation mechanism and similarities with TASEP. • Details of TASEP and the various aspects of translation it can explain. • Application of TASEP to transcription, motor transport and fungal growth. • Survey of the experiments that uncovered details of the translation mechanism. • How the large scale experimental data have been used in TASEP. • Limitations and future perspective. [ABSTRACT FROM AUTHOR]

We present a method to infer the arbitrary space-dependent drift and diffusion of a nonlinear stochastic model driven by multiplicative fractional Gaussian noise from a single trajectory. Our method, fractional Onsager-Machlup optimisation (fOMo), introduces a maximum likelihood estimator by minimising a field-theoretic action which we construct from the observed time series. We successfully test fOMo for a wide range of Hurst exponents using artificial data with strong nonlinearities, and apply it to a data set of daily mean temperatures. We further highlight the significant systematic estimation errors when ignoring non-Markovianity, underlining the need for nonlinear fractional inference methods when studying real-world long-range (anti-)correlated systems.

The article presents a study which explores the empirical mean-field model of symmetry-breaking in a turbulent wake , along with mentions that improved turbulence modeling remains a major open problem in mathematical physics. It discusses the work suggests a path toward low-dimensional models of globally unstable turbulent flows from experimental measurements, with broad implications for other multiscale systems.

This review is devoted to the universal algebraic and geometric properties of the non-relativistic quantum current algebra symmetry and to their representations subject to applications in describing geometrical and analytical properties of quantum and classical integrable Hamiltonian systems of theoretical and mathematical physics. The Fock space, the non-relativistic quantum current algebra symmetry and its cyclic representations on separable Hilbert spaces are reviewed and described in detail. The unitary current algebra family of operators and generating functional equations are described. A generating functional method to constructing irreducible current algebra representations is reviewed, and the ergodicity of the corresponding representation Hilbert space measure is mentioned. The algebraic properties of the so called coherent states are also reviewed, generated by cyclic representations of the Heisenberg algebra on Hilbert spaces. Unbelievable and impressive applications of coherent states to the theory of nonlinear dynamical systems on Hilbert spaces are described, along with their linearization and integrability. Moreover, we present a further development of these results within the modern Lie-algebraic approach to nonlinear dynamical systems on Poissonian functional manifolds, which proved to be both unexpected and important for the classification of integrable Hamiltonian flows on Hilbert spaces. The quantum current Lie algebra symmetry properties and their functional representations, interpreted as a universal algebraic structure of symmetries of completely integrable nonlinear dynamical systems of theoretical and mathematical physics on functional manifolds, are analyzed in detail. Based on the current algebra symmetry structure and their functional representations, an effective integrability criterion is formulated for a wide class of completely integrable Hamiltonian systems on functional manifolds. The related algebraic structure of the Poissonian operators and an effective algorithm of their analytical construction are described. The current algebra representations in separable Hilbert spaces and the factorized structure of quantum integrable many-particle Hamiltonian systems are reviewed. The related current algebra-based Hamiltonian reconstruction of the many-particle oscillatory and Calogero–Moser–Sutherland quantum models are reviewed and discussed in detail. The related quasi-classical quantum current algebra density representations and the collective variable approach in equilibrium statistical physics are reviewed. In addition, the classical Wigner type current algebra representation and its application to non-equilibrium classical statistical mechanics are described, and the construction of the Lie–Poisson structure on the phase space of the infinite hierarchy of distribution functions is presented. The related Boltzmann–Bogolubov type kinetic equation for the generating functional of many-particle distribution functions is constructed, and the invariant reduction scheme, compatible with imposed correlation functions constraints, is suggested and analyzed in detail. We also review current algebra functional representations and their geometric structure subject to the analytical description of quasi-stationary hydrodynamic flows and their magneto-hydrodynamic generalizations. A unified geometric description of the ideal idiabatic liquid dynamics is presented, and its Hamiltonian structure is analyzed. A special chapter of the review is devoted to recent results on the description of modified current Lie algebra symmetries on torus and their Lie-algebraic structures, related to integrable so-called heavenly type spatially many-dimensional dynamical systems on functional manifolds. [ABSTRACT FROM AUTHOR]

An established formal way to solve the Liouville equation for dynamical systems with hermitian Hamiltonians is to frame the problem via parametrization in a canonical ensemble. The goal is to solve for the dynamics in the canonical ensemble. This approach allows one to examine temperature-dependent relaxation functions as a part of the construction of the full solution to the Liouville equation. Of course, a full solution to the Liouville equation may not always be necessary as it is the relaxation function which often tends to be of interest. The formalism involves evaluation of infinite continued fractions and this can at times be an especially challenging problem. Here we explore the relaxation function in near-equilibrium systems in the presence of strong nonlinearity. Due to computational challenges, we consider small systems. However, even our small system considerations may be adequate to infer about the relaxation in large systems. We show that the nonlinear systems tend to lead to nonconvergent infinite continued fractions. These continued fractions cannot be approximated perturbatively. We close by presenting a new strategy to evaluate them, thereby demonstrating a successful way to examine relaxation processes in strongly nonlinear systems. [ABSTRACT FROM AUTHOR]