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BEGIN:VEVENT
UID:e858ee7005070618925222197793567c
CATEGORIES:Mathematical Physics Seminar
CREATED:20220630T103317
SUMMARY:From stochastic thermodynamics to thermodynamic inference
LOCATION:Zoom
DESCRIPTION:Stochastic thermodynamics provides a universal framework for analyzing nano
 - and micro-sized non-equilibrium systems. Prominent examples are single mo
 lecules, molecular machines, colloidal particles in time-dependent laser tr
 aps and biochemical networks. Thermodynamic notions like work, heat\nand en
 tropy can be identified on the level of individual fluctuating trajectories
 . They obey universal relations like the fluctuation theorem.\nThermodynami
 c inference as a general strategy uses consistency constraints derived from
  stochastic thermodynamics to infer otherwise hidden properties of non-equi
 librium systems. As a paradigm for thermodynamic inference, the thermodynam
 ic uncertainty relation provides a lower bound on the entropy production th
 rough measurements of the mean and dispersion of any current in the system.
  Likewise, it provides a model-free bound on the thermodynamic efficiency o
 f molecular motors. Waiting-time distributions between consecutive transiti
 ons in a discrete Markov network yield an even better estimator of entropy 
 production. Moreover, they reveal further information about the topology of
  the underlying network. From the observation of coherent oscillations, a u
 niversal bound on their thermodynamic cost can be deduced.\n
X-ALT-DESC;FMTTYPE=text/html:<p>Stochastic thermodynamics provides a universal framework for analyzing n
 ano- and micro-sized non-equilibrium systems. Prominent examples are single
  molecules, molecular machines, colloidal particles in time-dependent laser
  traps and biochemical networks. Thermodynamic notions like work, heat<br /
 >and entropy can be identified on the level of individual fluctuating traje
 ctories. They obey universal relations like the fluctuation theorem.</p><p>
 Thermodynamic inference as a general strategy uses consistency constraints 
 derived from stochastic thermodynamics to infer otherwise hidden properties
  of non-equilibrium systems. As a paradigm for thermodynamic inference, the
  thermodynamic uncertainty relation provides a lower bound on the entropy p
 roduction through measurements of the mean and dispersion of any current in
  the system. Likewise, it provides a model-free bound on the thermodynamic 
 efficiency of molecular motors. Waiting-time distributions between consecut
 ive transitions in a discrete Markov network yield an even better estimator
  of entropy production. Moreover, they reveal further information about the
  topology of the underlying network. From the observation of coherent oscil
 lations, a universal bound on their thermodynamic cost can be deduced.</p>
CONTACT:Udo Seifert - Universitaet Stuttgart
DTSTAMP:20260930T011535
DTSTART;TZID=America/New_York:20220720T104500
DTEND;TZID=America/New_York:20220720T114500
SEQUENCE:0
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