The Experts below are selected from a list of 111315 Experts worldwide ranked by ideXlab platform
Daniel G. Friend - One of the best experts on this subject based on the ideXlab platform.
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A Helmholtz Free Energy Formulation of the Thermodynamic Properties of the Mixture (Water + Ammonia)
Journal of Physical and Chemical Reference Data, 1998Co-Authors: Reiner Tillner-roth, Daniel G. FriendAbstract:A thermodynamic model incorporating a fundamental equation of state for the Helmholtz free Energy of the mixture {water+ammonia} is presented which covers the thermodynamic space between the solid–liquid–vapor boundary and the critical locus. It is also valid in the vapor and liquid phases for pressures up to 40 MPa. It represents vapor–liquid equilibrium properties with an uncertainty of ±0.01 in liquid and vapor mole fractions. Typical uncertainties in the single-phase regions are ±0.3% for the density and ±200 J mol−1 for enthalpies. Details of the data selection and the optimization process are given. The behavior of the fundamental equation of state is discussed in all parts of the thermodynamic space.A thermodynamic model incorporating a fundamental equation of state for the Helmholtz free Energy of the mixture {water+ammonia} is presented which covers the thermodynamic space between the solid–liquid–vapor boundary and the critical locus. It is also valid in the vapor and liquid phases for pressures up to 40 MPa. It represents vapor–liquid equilibrium properties with an uncertainty of ±0.01 in liquid and vapor mole fractions. Typical uncertainties in the single-phase regions are ±0.3% for the density and ±200 J mol−1 for enthalpies. Details of the data selection and the optimization process are given. The behavior of the fundamental equation of state is discussed in all parts of the thermodynamic space.
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a helmholtz free Energy Formulation of the thermodynamic properties of the mixture water ammonia
Journal of Physical and Chemical Reference Data, 1998Co-Authors: Reiner Tillnerroth, Daniel G. FriendAbstract:A thermodynamic model incorporating a fundamental equation of state for the Helmholtz free Energy of the mixture {water+ammonia} is presented which covers the thermodynamic space between the solid–liquid–vapor boundary and the critical locus. It is also valid in the vapor and liquid phases for pressures up to 40 MPa. It represents vapor–liquid equilibrium properties with an uncertainty of ± 0.01 in liquid and vapor mole fractions. Typical uncertainties in the single-phase regions are ± 0.3% for the density and ±200 J mol −1 for enthalpies. Details of the data selection and the optimization process are given. The behavior of the fundamental equation of state is discussed in all parts of the thermodynamic space.
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A Helmholtz Free Energy Formulation of the Thermodynamic Properties of the Mixture {Water + Ammonia}
Journal of Physical and Chemical Reference Data, 1998Co-Authors: Reiner Tillner-roth, Daniel G. FriendAbstract:A thermodynamic model incorporating a fundamental equation of state for the Helmholtz free Energy of the mixture {water+ammonia} is presented which covers the thermodynamic space between the solid–liquid–vapor boundary and the critical locus. It is also valid in the vapor and liquid phases for pressures up to 40 MPa. It represents vapor–liquid equilibrium properties with an uncertainty of ±0.01 in liquid and vapor mole fractions. Typical uncertainties in the single-phase regions are ±0.3% for the density and ±200 J mol−1 for enthalpies. Details of the data selection and the optimization process are given. The behavior of the fundamental equation of state is discussed in all parts of the thermodynamic space.
Karl J Friston - One of the best experts on this subject based on the ideXlab platform.
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The hierarchically mechanistic mind: A free-Energy Formulation of the human psyche.
Physics of Life Reviews, 2019Co-Authors: Paul B Badcock, Karl J Friston, Maxwell J D RamsteadAbstract:This article presents a unifying theory of the embodied, situated human brain called the Hierarchically Mechanistic Mind (HMM). The HMM describes the brain as a complex adaptive system that actively minimises the decay of our sensory and physical states by producing self-fulfilling action-perception cycles via dynamical interactions between hierarchically organised neurocognitive mechanisms. This theory synthesises the free-Energy principle (FEP) in neuroscience with an evolutionary systems theory of psychology that explains our brains, minds, and behaviour by appealing to Tinbergen's four questions: adaptation, phylogeny, ontogeny, and mechanism. After leveraging the FEP to formally define the HMM across different spatiotemporal scales, we conclude by exploring its implications for theorising and research in the sciences of the mind and behaviour.
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answering schrodinger s question a free Energy Formulation
Physics of Life Reviews, 2017Co-Authors: Maxwell J D Ramstead, Paul B Badcock, Karl J FristonAbstract:The free-Energy principle (FEP) is a formal model of neuronal processes that is widely recognised in neuroscience as a unifying theory of the brain and biobehaviour. More recently, however, it has been extended beyond the brain to explain the dynamics of living systems, and their unique capacity to avoid decay. The aim of this review is to synthesise these advances with a meta-theoretical ontology of biological systems called variational neuroethology, which integrates the FEP with Tinbergen's four research questions to explain biological systems across spatial and temporal scales. We exemplify this framework by applying it to Homo sapiens, before translating variational neuroethology into a systematic research heuristic that supplies the biological, cognitive, and social sciences with a computationally tractable guide to discovery.
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a free Energy Formulation of music generation and perception helmholtz revisited
2013Co-Authors: Karl J Friston, Dominic FristonAbstract:This chapter pursues the notion that, quintessentially, music enables the prediction of the unpredictable. Our focus is on the perception of music using ideas from theoretical biology and neuroscience to explain the nature of musical stimuli and their perceptual synthesis. In brief, we will consider music as a perceptual construct that supports (unconscious) inference on the causal structure of auditory input, in the sense of Helmholtz (1860). We examine the motivation for this particular perspective on music and consider the neuronal architectures that underlie its perception. The basic premises and supposed neuronal implementation—in terms of embodied inference—are then illustrated using simulations of (bird) song generation and perception; with a special focus on reproducing perceptual and neurophysiological responses that are seen in empirical neuroscience studies.
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action and behavior a free Energy Formulation
Biological Cybernetics, 2010Co-Authors: Karl J Friston, Jean Daunizeau, James M Kilner, Stephan J KiebelAbstract:We have previously tried to explain perceptual inference and learning under a free-Energy principle that pursues Helmholtz’s agenda to understand the brain in terms of Energy minimization. It is fairly easy to show that making inferences about the causes of sensory data can be cast as the minimization of a free-Energy bound on the likelihood of sensory inputs, given an internal model of how they were caused. In this article, we consider what would happen if the data themselves were sampled to minimize this bound. It transpires that the ensuing active sampling or inference is mandated by ergodic arguments based on the very existence of adaptive agents. Furthermore, it accounts for many aspects of motor behavior; from retinal stabilization to goal-seeking. In particular, it suggests that motor control can be understood as fulfilling prior expectations about proprioceptive sensations. This Formulation can explain why adaptive behavior emerges in biological agents and suggests a simple alternative to optimal control theory. We illustrate these points using simulations of oculomotor control and then apply to same principles to cued and goal-directed movements. In short, the free-Energy Formulation may provide an alternative perspective on the motor control that places it in an intimate relationship with perception.
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Reinforcement learning or active inference?
PLoS ONE, 2009Co-Authors: Karl J Friston, Jean Daunizeau, Stephan J KiebelAbstract:This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-Energy Formulation of perception. In this Formulation, agents adjust their internal states and sampling of the environment to minimize their free-Energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-Energy principle. The ensuing proof-of-concept may be important because the free-Energy Formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain.
Marc Alexa - One of the best experts on this subject based on the ideXlab platform.
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as rigid as possible surface modeling
Symposium on Geometry Processing, 2007Co-Authors: Olga Sorkine, Marc AlexaAbstract:Modeling tasks, such as surface deformation and editing, can be analyzed by observing the local behavior of the surface. We argue that defining a modeling operation by asking for rigidity of the local transformations is useful in various settings. Such Formulation leads to a non-linear, yet conceptually simple Energy Formulation, which is to be minimized by the deformed surface under particular modeling constraints. We devise a simple iterative mesh editing scheme based on this principle, that leads to detail-preserving and intuitive deformations. Our algorithm is effective and notably easy to implement, making it attractive for practical modeling applications.
Stephan J Kiebel - One of the best experts on this subject based on the ideXlab platform.
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action and behavior a free Energy Formulation
Biological Cybernetics, 2010Co-Authors: Karl J Friston, Jean Daunizeau, James M Kilner, Stephan J KiebelAbstract:We have previously tried to explain perceptual inference and learning under a free-Energy principle that pursues Helmholtz’s agenda to understand the brain in terms of Energy minimization. It is fairly easy to show that making inferences about the causes of sensory data can be cast as the minimization of a free-Energy bound on the likelihood of sensory inputs, given an internal model of how they were caused. In this article, we consider what would happen if the data themselves were sampled to minimize this bound. It transpires that the ensuing active sampling or inference is mandated by ergodic arguments based on the very existence of adaptive agents. Furthermore, it accounts for many aspects of motor behavior; from retinal stabilization to goal-seeking. In particular, it suggests that motor control can be understood as fulfilling prior expectations about proprioceptive sensations. This Formulation can explain why adaptive behavior emerges in biological agents and suggests a simple alternative to optimal control theory. We illustrate these points using simulations of oculomotor control and then apply to same principles to cued and goal-directed movements. In short, the free-Energy Formulation may provide an alternative perspective on the motor control that places it in an intimate relationship with perception.
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Reinforcement learning or active inference?
PLoS ONE, 2009Co-Authors: Karl J Friston, Jean Daunizeau, Stephan J KiebelAbstract:This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-Energy Formulation of perception. In this Formulation, agents adjust their internal states and sampling of the environment to minimize their free-Energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-Energy principle. The ensuing proof-of-concept may be important because the free-Energy Formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain.
Reiner Tillnerroth - One of the best experts on this subject based on the ideXlab platform.
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a helmholtz free Energy Formulation of the thermodynamic properties of the mixture water ammonia
Journal of Physical and Chemical Reference Data, 1998Co-Authors: Reiner Tillnerroth, Daniel G. FriendAbstract:A thermodynamic model incorporating a fundamental equation of state for the Helmholtz free Energy of the mixture {water+ammonia} is presented which covers the thermodynamic space between the solid–liquid–vapor boundary and the critical locus. It is also valid in the vapor and liquid phases for pressures up to 40 MPa. It represents vapor–liquid equilibrium properties with an uncertainty of ± 0.01 in liquid and vapor mole fractions. Typical uncertainties in the single-phase regions are ± 0.3% for the density and ±200 J mol −1 for enthalpies. Details of the data selection and the optimization process are given. The behavior of the fundamental equation of state is discussed in all parts of the thermodynamic space.