The University of Tokyo · 물리·천문학
Shun Otsubo 교수의 연구실은 비평형 열역학과 확률적 동역학을 기반으로 한 비평형 시스템의 엔트로피 생성률 추정에 중점을 두고 있습니다. 특히 시간에 의존하는 비평형 동역학에서 엔트로피 생성률을 정확하게 추정할 수 있는 이론적 프레임워크와 기계학습 기반의 데이터 기반 추정 기법을 개발하고 있습니다. 연구는 실험적 시간 시리즈 데이터로부터 비평형 상태를 분석하고, 열역학적 불확실성 원리(TUR) 및 변분 특성화 기반 방법을 활용하여 엔트로피 생성을 정량화하는 데 초점을 맞추고 있습니다. 이는 자율주행 차량 제어나 생물학적 시스템 분석 등 응용 분야로까지 확장 가능합니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
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
