Tag: Sreekanth K. Manikandan

The first and second laws of thermodynamics provide the basic principles for describing energy transformations and the emergence of the arrow of time in physical systems. Heat dissipation and entropy production, central quantities in these laws, are often difficult to quantify precisely in experiments because the associated energy exchanges are distributed over a vast number of typically inaccessible degrees of freedom in the environment. This is especially true at microscopic scales, where the relevant energy scales are often of the order of kB T, comparable to thermal fluctuations in the environment, and can lie several orders of magnitude below the sensitivity limits of state-of-the-art room-temperature calorimetry.

Recent advances in nonequilibrium statistical physics and data-driven inference provide new approaches for addressing these challenges. In this talk, I will first introduce recent theoretical results showing how nonequilibrium fluctuations can be exploited to quantify energy dissipation at scales inaccessible to conventional calorimetry. I will then demonstrate the practical applicability of these results by quantifying energy dissipation from experimental data in real physical systems, as well as estimating free-energy differences from nonequilibrium measurements in highly dissipative regimes where conventional fluctuation-theorem-based methods fail. I will conclude by discussing the remaining challenges and open questions.

The first and second laws of thermodynamics provide the basic principles for describing energy transformations and the emergence of the arrow of time in physical systems. Heat dissipation and entropy production, central quantities in these laws, are often difficult to quantify precisely in experiments because the associated energy exchanges are distributed over a vast number of typically inaccessible degrees of freedom in the environment. This is especially true at microscopic scales, where the relevant energy scales are often of the order of kB T, comparable to thermal fluctuations in the environment, and can lie several orders of magnitude below the sensitivity limits of state-of-the-art room-temperature calorimetry.

Recent advances in nonequilibrium statistical physics and data-driven inference provide new approaches for addressing these challenges. In this talk, I will first introduce recent theoretical results showing how nonequilibrium fluctuations can be exploited to quantify energy dissipation at scales inaccessible to conventional calorimetry. I will then demonstrate the practical applicability of these results by quantifying energy dissipation from experimental data in real physical systems, as well as estimating free-energy differences from nonequilibrium measurements in highly dissipative regimes where conventional fluctuation-theorem-based methods fail. I will conclude by discussing the remaining challenges and open questions.

Identifying whether a process is in equilibrium, quantifying its distance from equilibrium, and constructing optimal reduced descriptions of non-equilibrium dynamics remain central challenges in the study of living matter. Here, we discuss how data-driven approaches grounded in stochastic thermodynamics enable these features to be inferred directly from experimental data. In particular, we show how entropy production can be localized in space and time, and how maximally dissipative coordinates emerge as effective low-dimensional descriptions of non-equilibrium processes. We highlight applications to experimental biophysical systems and discuss key challenges and limitations.

Identifying whether a process is in equilibrium, quantifying how far it lies from equilibrium, and determining optimal reduced descriptions of non-equilibrium processes remain challenging open problems. Here, we discuss how novel data-driven techniques grounded in stochastic thermodynamics can be used to efficiently learn these features directly from experimental data. In particular, we show how entropy production can be localized in space and time, and how maximally dissipative coordinates can be consistently inferred as effective low-dimensional descriptions of non-equilibrium processes. We further discuss applications to experimental biophysical systems and outline key challenges and limitations.

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Quantifying the spatiotemporal forces, affinities, and dissipative costs of cellular-scale non-equilibrium processes from experimental data and localizing it in space and time remain a significant open challenge. Here, I explore how principles from stochastic thermodynamics, combined with machine learning techniques, offer a promising approach to addressing this issue. I will present preliminary results from experiments on fluctuating cell membranes and simulations of non-equilibrium systems in stationary and time-dependently driven states. These studies reveal potential strategies for localizing entropy production in experimental biophysical contexts while also highlighting key challenges and limitations that must be addressed.

Quantifying the spatiotemporal forces, affinities, and dissipative costs of cellular-scale non-equilibrium processes from experimental data and localizing it in space and time remain a significant open challenge. Here, I explore how principles from stochastic thermodynamics, combined with machine learning techniques, offer a promising approach to addressing this issue. I will present preliminary results from experiments on fluctuating cell membranes and simulations of non-equilibrium systems in stationary and time-dependently driven states. These studies reveal potential strategies for localizing entropy production in experimental biophysical contexts while also highlighting key challenges and limitations that must be addressed.

Quantifying the spatiotemporal forces, affinities, and dissipative costs of cellular-scale non-equilibrium processes from experimental data and localizing it in space and time remain a significant open challenge. Here, I explore how principles from stochastic thermodynamics, combined with machine learning techniques, offer a promising approach to addressing this issue. I will present preliminary results from experiments on fluctuating cell membranes and simulations of non-equilibrium systems in stationary and time-dependently driven states. These studies reveal potential strategies for localizing entropy production in experimental biophysical contexts while also highlighting key challenges and limitations that must be addressed.