Lecture
Entropy and Disorder
In course
DEMO: consequat aute consequat
Sit commodo enim commodo amet fugiat. Magna sunt aliquip ut ad dolore consequat aliqua id amet consequat dolore velit ut esse. Nisi aute minim laboris quis ipsum eiusmod. Ullamco cupidatat consectetur quis Lorem laboris consequat sit elit qui nostrud do. Nisi ipsum non sit pariatur duis incididunt cillum est ad. Aliqua fugiat occaecat id dolore amet proident commodo ea. Sunt labore sint elit velit irure culpa qui sint aute adipisicing magna ullamco ad ut.
Description
This lecture covers the concepts of entropy and disorder, starting with the definition of entropy according to Shannon. It then explores the relationship between entropy and disorder, illustrating examples of disorder and explaining how the measurement of disorder depends solely on the data of the subsystems. The lecture also delves into the concept of maximum disorder, the additive nature of disorder, and the calculation of disorder in different scenarios. Furthermore, it introduces the theorem related to finding the maximum entropy under certain constraints using the Lagrange multipliers method.
Instructor
fugiat cillum
Elit sunt pariatur irure officia mollit aute. Tempor non non ullamco occaecat exercitation culpa fugiat irure ea exercitation mollit laborum. Sunt adipisicing occaecat amet Lorem nostrud do ullamco officia labore cupidatat eu sunt pariatur. Sint velit cupidatat proident quis laborum in duis. Non voluptate sint pariatur non deserunt deserunt qui sint sit.
Official source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Ontological neighbourhood
Related lectures (33)
Entropy and the Second Law of Thermodynamics
Covers entropy, its definition, and its implications in thermodynamics.
Introduction to Data Science
Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.
Statistical Physics: Systems Isolation
Explores statistical physics concepts in isolated systems, focusing on entropy and disorder.
Quantum Information
Explores the CHSH operator, self-testing, eigenstates, and quantifying randomness in quantum systems.
Statistical Entropy and Disorder
Explores statistical entropy and disorder in isolated systems, including kinetic gases and calorimetric coefficients.