The University of Tokyo · Physics and Astronomy
Professor Shun Otsubo's research lab specializes in statistical physics and nonequilibrium stochastic processes, with a focus on quantifying irreversibility and entropy production in non-equilibrium systems. The lab develops theoretical frameworks and data-driven methods—particularly combining thermodynamic uncertainty relations, variational principles, and machine learning—to estimate entropy production from limited time-series data, even in non-stationary or transient dynamics. Their work bridges fundamental nonequilibrium thermodynamics with practical applications in experimental physics, biophysics, and autonomous systems. A key direction involves creating efficient, model-free estimation techniques for entropy production using trajectory data, enabling real-world applications in complex systems.
Figures are computed from collected data and may differ slightly.
Thermodynamic uncertainty relations (TURs) are the inequalities which give lower bounds on the entropy production rate using only the mean and the variance of fluctuating currents. Since the TURs do not refer to the full details of the stochastic dynamics, it would be promising to apply the TURs for estimating the entropy production rate from a limited set of trajectory data corresponding to the dynamics. Here we investigate a theoretical framework for estimation of the entropy production rate u
Abstract The rate of entropy production provides a useful quantitative measure of a non-equilibrium system and estimating it directly from time-series data from experiments is highly desirable. Several approaches have been considered for stationary dynamics, some of which are based on a variational characterization of the entropy production rate. However, the issue of obtaining it in the case of non-stationary dynamics remains largely unexplored. Here, we solve this open problem by demonstrating
The rate of entropy production provides a useful quantitative measure of a non-equilibrium system and estimating it directly from time-series data from experiments is highly desirable. Several approaches have been considered for stationary dynamics, some of which are based on a variational characterization of the entropy production rate. However the issue of obtaining it in the case of non-stationary dynamics remains largely unexplored. Here, we solve this open problem by demonstrating that the
This paper proposes an automatic driving system based on a combination of modular neural networks processing human driving data. Research on automatic driving vehicles has been actively conducted in recent years. Machine learning techniques are often utilized to realize an automatic driving system capable of imitating human driving operations. Almost all of them adopt a large monolithic learning module, as typified by deep learning. However, it is inefficient to use a monolithic deep learning mo
This repository includes all the data used in our paper "Estimating time-dependent entropy production from non-equilibrium trajectories" (https://arxiv.org/abs/2010.03852), which will be published from Communications Physics.
This repository includes all the data used in our paper "Estimating entropy production along a single non-equilibrium trajectory" (https://arxiv.org/abs/2010.03852).
This repository includes all the data used in our paper "Estimating entropy production along a single non-equilibrium trajectory" (https://arxiv.org/abs/2010.03852).
Recent progress in experimental techniques has enabled us to quantitatively study stochastic and flexible behavior of biological systems. For example, gene regulatory networks perform stochastic information processing and their functionalities have been extensively studied. In gene regulatory networks, there are specific subgraphs called network motifs that occur at frequencies much higher than those found in randomized networks. Further understanding of the designing principle of such networks
Thermodynamic uncertainty relations (TURs) are the inequalities which give lower bounds on the entropy production rate using only the mean and the variance of fluctuating currents. Since the TURs do not refer to the full details of the stochastic dynamics, it would be promising to apply the TURs for estimating the entropy production rate from a limited set of trajectory data corresponding to the dynamics. Here we investigate a theoretical framework for estimation of the entropy production rate u
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