By Arieh Ben-Naim

The imperative message of this e-book is that thermodynamics and statistical mechanics will take advantage of changing the unlucky, deceptive and mysterious time period “entropy” with a extra prevalent, significant and acceptable time period reminiscent of info, lacking details or uncertainty. This alternative could facilitate the translation of the “driving strength” of many methods by way of informational alterations and dispel the secret that has consistently enshrouded entropy.

It has been one hundred forty years for the reason that Clausius coined the time period “entropy”; virtually 50 years considering that Shannon constructed the mathematical thought of “information” — in this case renamed “entropy”. during this ebook, the writer advocates changing “entropy” via “information”, a time period that has develop into ordinary in lots of branches of science.

The writer additionally takes a brand new and ambitious method of thermodynamics and statistical mechanics. details is used not just as a device for predicting distributions yet because the basic cornerstone inspiration of thermodynamics, held before by way of the time period “entropy”.

The themes lined comprise the basics of likelihood and data idea; the final notion of data in addition to the actual inspiration of data as utilized in thermodynamics; the re-derivation of the Sackur-Tetrode equation for the entropy of a great fuel from only informational arguments; the basic formalism of statistical mechanics; and plenty of examples of straightforward tactics the “driving strength” for that is analyzed when it comes to information.


  • Elements of likelihood Theory
  • Elements of knowledge Theory
  • Transition from the final MI to the Thermodynamic MI
  • The constitution of the principles of Statistical Thermodynamics
  • Some uncomplicated Applications

Readership: someone drawn to the sciences, scholars, researchers; in addition to the layman.

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A Farewell to Entropy:Statistical Thermodynamics Based on Information by Arieh Ben-Naim

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