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Author: Takahiro Sagawa Publisher: Springer Science & Business Media ISBN: 4431541683 Category : Science Languages : en Pages : 126
Book Description
This thesis presents a general theory of nonequilibrium thermodynamics for information processing. Ever since Maxwell's demon was proposed in the nineteenth century, the relationship between thermodynamics and information has attracted much attention because it concerns the foundation of the second law of thermodynamics. From the modern point of view, Maxwell's demon is formulated as an information processing device that performs measurement and feedback at the level of thermal fluctuations. By unifying information theory, measurement theory, and the recently developed theory of nonequilibrium statistical mechanics, the author has constructed a theory of "information thermodynamics," in which information contents and thermodynamic variables are treated on an equal footing. In particular, the maximum work that can be extracted by the demon and the minimum work that is needed for measurement and information erasure by the demon has been determined. Additionally, generalizations of nonequilibrium relations such as a Jarzynski equality for classical stochastic systems in the presence of feedback control have been derived. One of the generalized equalities has recently been verified experimentally by using sub-micron colloidal particles. The results obtained serve as fundamental principles for information processing in small thermodynamic systems, and are applicable to nanomachines and nanodevices.
Author: Takahiro Sagawa Publisher: Springer Science & Business Media ISBN: 4431541683 Category : Science Languages : en Pages : 126
Book Description
This thesis presents a general theory of nonequilibrium thermodynamics for information processing. Ever since Maxwell's demon was proposed in the nineteenth century, the relationship between thermodynamics and information has attracted much attention because it concerns the foundation of the second law of thermodynamics. From the modern point of view, Maxwell's demon is formulated as an information processing device that performs measurement and feedback at the level of thermal fluctuations. By unifying information theory, measurement theory, and the recently developed theory of nonequilibrium statistical mechanics, the author has constructed a theory of "information thermodynamics," in which information contents and thermodynamic variables are treated on an equal footing. In particular, the maximum work that can be extracted by the demon and the minimum work that is needed for measurement and information erasure by the demon has been determined. Additionally, generalizations of nonequilibrium relations such as a Jarzynski equality for classical stochastic systems in the presence of feedback control have been derived. One of the generalized equalities has recently been verified experimentally by using sub-micron colloidal particles. The results obtained serve as fundamental principles for information processing in small thermodynamic systems, and are applicable to nanomachines and nanodevices.
Author: Nathan Crock Publisher: ISBN: Category : Information science Languages : en Pages : 0
Book Description
An accumulation of experimental evidence over the past two decades has tightened the relationship between thermodynamics and the emergence of structure in driven natural systems. A plausible explanation for how structure emerges has arisen from the theory of stochastic thermodynamics called dissipative adaptation. As thermodynamic systems transition between states due to thermal fluctuations and external agency, the system is biased towards states that efficiently absorb energy from the applied force. The theory shows promise in explaining many challenging concepts such as phase transitions and evolutionary adaptation. However, nature is replete with systems that augment their configuration and act on their environment with apparent intent. In its current formulation, dissipative adaptation offers no insight into the mechanisms underlying this emergent feedback behavior. In this work, we propose that by integrating recent advances in stochastic process research and information theory we can extend this first principles approach to study the emergence of information processing. We propose a novel probabilistic graphical model that captures the underlying principles of stochastic thermodynamic systems capable of measurement. The model is based on an emerging discipline that integrates information into stochastic thermodynamics known as thermodynamics of information. Modern work in stochastic optimization theory provides a provably convergent stochastic approximation algorithm based on the Robbins-Munro algorithm that follows from the extended second law of thermodynamics. Finally, we will examine loosely coupled networks of individual systems that obey our stochastic optimization scheme while being stimulated by a transient data generating distribution and observe the hypothesized emergent behavior.
Author: Luca Peliti Publisher: Princeton University Press ISBN: 0691201773 Category : Mathematics Languages : en Pages : 272
Book Description
The first comprehensive graduate-level introduction to stochastic thermodynamics Stochastic thermodynamics is a well-defined subfield of statistical physics that aims to interpret thermodynamic concepts for systems ranging in size from a few to hundreds of nanometers, the behavior of which is inherently random due to thermal fluctuations. This growing field therefore describes the nonequilibrium dynamics of small systems, such as artificial nanodevices and biological molecular machines, which are of increasing scientific and technological relevance. This textbook provides an up-to-date pedagogical introduction to stochastic thermodynamics, guiding readers from basic concepts in statistical physics, probability theory, and thermodynamics to the most recent developments in the field. Gradually building up to more advanced material, the authors consistently prioritize simplicity and clarity over exhaustiveness and focus on the development of readers’ physical insight over mathematical formalism. This approach allows the reader to grow as the book proceeds, helping interested young scientists to enter the field with less effort and to contribute to its ongoing vibrant development. Chapters provide exercises to complement and reinforce learning. Appropriate for graduate students in physics and biophysics, as well as researchers, Stochastic Thermodynamics serves as an excellent initiation to this rapidly evolving field. Emphasizes a pedagogical approach to the subject Highlights connections with the thermodynamics of information Pays special attention to molecular biophysics applications Privileges physical intuition over mathematical formalism Solutions manual available on request for instructors adopting the book in a course
Author: Naoto Shiraishi Publisher: Springer Nature ISBN: 9811981868 Category : Science Languages : en Pages : 437
Book Description
This book presents the fundamentals of stochastic thermodynamics, one of the most central subjects in non-equilibrium statistical mechanics. It also explores many recent advances, e.g., in information thermodynamics, the thermodynamic uncertainty relation, and the trade-off relation between efficiency and power. The content is divided into three main parts, the first of which introduces readers to fundamental topics in stochastic thermodynamics, e.g., the basics of stochastic processes, the fluctuation theorem and its variants, information thermodynamics, and large deviation theory. In turn, parts two and three explore advanced topics such as autonomous engines (engines not controlled externally) and finite speed engines, while also explaining the key concepts from recent stochastic thermodynamics theory that are involved. To fully benefit from the book, readers only need an undergraduate-level background in statistical mechanics and quantum mechanics; no background in information theory or stochastic processes is needed. Accordingly, the book offers a valuable resource for early graduate or higher-level readers who are unfamiliar with this subject but want to keep up with the cutting-edge research in this field. In addition, the author’s vivid descriptions interspersed throughout the book will help readers grasp ‘living’ research developments and begin their own research in this field.
Author: Sosuke Ito Publisher: Springer ISBN: 981101664X Category : Science Languages : en Pages : 140
Book Description
In this book the author presents a general formalism of nonequilibrium thermodynamics with complex information flows induced by interactions among multiple fluctuating systems. The author has generalized stochastic thermodynamics with information by using a graphical theory. Characterizing nonequilibrium dynamics by causal networks, he has obtained a novel generalization of the second law of thermodynamics with information that is applicable to quite a broad class of stochastic dynamics such as information transfer between multiple Brownian particles, an autonomous biochemical reaction, and complex dynamics with a time-delayed feedback control. This study can produce further progress in the study of Maxwell’s demon for special cases. As an application to these results, information transmission and thermodynamic dissipation in biochemical signal transduction are discussed. The findings presented here can open up a novel biophysical approach to understanding information processing in living systems.
Author: Joel Keizer Publisher: Springer Science & Business Media ISBN: 1461210542 Category : Science Languages : en Pages : 517
Book Description
The structure of the theory ofthermodynamics has changed enormously since its inception in the middle of the nineteenth century. Shortly after Thomson and Clausius enunciated their versions of the Second Law, Clausius, Maxwell, and Boltzmann began actively pursuing the molecular basis of thermo dynamics, work that culminated in the Boltzmann equation and the theory of transport processes in dilute gases. Much later, Onsager undertook the elucidation of the symmetry oftransport coefficients and, thereby, established himself as the father of the theory of nonequilibrium thermodynamics. Com bining the statistical ideas of Gibbs and Langevin with the phenomenological transport equations, Onsager and others went on to develop a consistent statistical theory of irreversible processes. The power of that theory is in its ability to relate measurable quantities, such as transport coefficients and thermodynamic derivatives, to the results of experimental measurements. As powerful as that theory is, it is linear and limited in validity to a neighborhood of equilibrium. In recent years it has been possible to extend the statistical theory of nonequilibrium processes to include nonlinear effects. The modern theory, as expounded in this book, is applicable to a wide variety of systems both close to and far from equilibrium. The theory is based on the notion of elementary molecular processes, which manifest themselves as random changes in the extensive variables characterizing a system. The theory has a hierarchical character and, thus, can be applied at various levels of molecular detail.
Author: Chris Kempes Publisher: Seminar ISBN: 9781947864184 Category : Science Languages : en Pages : 500
Book Description
Why do computers use so much energy? What are the fundamental physical laws governing the relationship between the precise computation run by a system, whether artificial or natural, and how much energy that computation requires? This volume integrates concepts from diverse fields, cultivating a modern, nonequilibrium thermodynamics of computation.
Author: Joseph Neil Lucero Publisher: ISBN: Category : Languages : en Pages : 132
Book Description
Stochastic thermodynamics is an emerging field of research that has received considerable attention in the past two decades. Among its most visible applications is to understand the connections between information and thermodynamics. Recent theoretical advances in this field have established that the second law of thermodynamics, suitably modified to account for information, sets the limits of information-to-energy conversion; however, these limits are generally derived for systems that are ideal and assume that all of the system's energy can be extracted. Real systems on the other hand face constraints that may prevent them, both in principle as well as in practice, from achieving the predicted theoretical limits. Prompted by recent advances in experimental capabilities which allow for a high degree of control of mesoscopic systems, we explore the limits of information-to-work conversion in a simple "textbook example" colloid-based information engine that is implementable in the lab. We use this engine to explore the limits of information-to-work conversion when the engine is restricted to operate in a mode where long-term energy storage is prioritized. We find that restricting the engine to this mode of operation severely limits its ability to convert information to work compared to when the engine is optimized for raw energy extraction, without regards for whether the energy is stored or not. Nevertheless, in certain cases, it is possible to design the feedback control to have a work input which guarantees the engine stores energy at the highest achievable rate. We therefore find that information engines sometimes convert information to work most effectively when there is a mixture of external work input and information processing. Additionally, real engines face the conundrum of measurement noise. This complicates the feedback control and introduces biases in the estimates of the relevant thermodynamic quantities. To eliminate this bias, we use either a filter or we introduce feedback delays. Both strategies successfully eliminate the bias in the estimates; however, we find that using the filter has an additional benefit in that it allows us to compute a trajectory-level estimate of the information-processing costs. These results inform our theoretical understanding of the limits of real systems that convert information to work and provides the first measure of the information-processing costs for continuous variables.
Author: Takahiro Sagawa
Author: Takahiro Sagawa
Author: Nathan Crock
Author: Luca Peliti
Author: Naoto Shiraishi
Author: David Hartich
Author: Sosuke Ito
Author: Joel Keizer
Author: Yiannis N. Kaznessis
Author: Chris Kempes
Author: Joseph Neil Lucero