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Item type:Publication, Access status: with files , Generalising thermodynamic efficiency of interactions: inferential, information-geometric and computational perspectivesSelf-organizing systems consume energy to generate internal order. The concept of thermodynamic efficiency, drawing from statistical physics and information theory, has previously been proposed to characterise a change in control parameter by relating the resulting predictability gain to the required amount of work. However, previous studies have taken a system-centric perspective and considered only single control parameters. Here, considering systems at or near equilibrium, we generalise thermodynamic efficiency to multiple control parameters and extend the definition of thermodynamic efficiency to protocols in arbitrary directions, by introducing directional efficiency. Taking an observer-centric perspective, we derive two novel formulations. The first, an inferential form, relates efficiency to fluctuations of macroscopic observables, interpreting thermodynamic efficiency in terms of how well the system parameters can be inferred from observable macroscopic behaviour. The second, an information-geometric form, expresses efficiency in terms of the Fisher information matrix, interpreting it with respect to how difficult it is to navigate the statistical manifold defined by the control protocol. This observer-centric perspective is contrasted with the existing system-centric view, where efficiency is considered an intrinsic property of the system.Publicationtype: Journal Article16 12 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Assessing the suitability of the Langevin equation for analyzing measured data through downsampling(2025-03-01); ; ; The measured time series from complex systems are renowned for their complex stochastic behavior, characterized by random fluctuations stemming from external influences and nonlinear interactions. These fluctuations take diverse forms, ranging from continuous trajectories reminiscent of Brownian motion to noncontinuous trajectories featuring jump events. The Langevin equation is a versatile framework for modeling stochastic systems, effectively describing the complex behavior of measured data that exhibit continuous stochastic variability and adhere to Markov properties. However, the traditional modeling framework of the Langevin equation falls short when it comes to capturing the presence of abrupt changes, particularly jumps, in trajectories that exhibit non-continuity. Such non-continuous changes pose a significant challenge for general processes and have profound implications for risk management. Moreover, the discrete nature of observed physical phenomena, measured with a finite sample rate, adds another layer of complexity. In such cases, data points often appear as a series of discontinuous jumps, even when the underlying trajectory is continuous. In this study, we present an analytical framework that goes beyond the limitations of the Langevin equation. Our approach effectively distinguishes between diffusive or Brownian-type trajectories and non-diffusive trajectories such as those with jumps. By introducing downsampling techniques, where we artificially lower the sample rate, we derive a set of measures and criteria to analyze the data and differentiate between diffusive and non-diffusive behaviors. To further demonstrate its versatility and practical applicability, we have applied our proposed method to real-world data in various scientific fields, such as trapped particles in optical tweezers, market price, neuroscience, turbulence and renewable energy. For real-world data that lack Markov properties, we estimate the functions and parameters using the generalized Langevin equation, which incorporates a memory kernel to account for non-Markovian dynamics.Publicationtype: Journal Article20 22