Course Identification

Physical Nature of Information
20231192

Lecturers and Teaching Assistants

Prof. Gregory Falkovich
Chen Mor, Gadi Trocki Reibstein

Course Schedule and Location

2023
Second Semester
Monday, 14:15 - 16:00, Weissman, Auditorium

Tutorials
Wednesday, 14:15 - 15:00, Weissman, Auditorium
19/04/2023
21/07/2023

Field of Study, Course Type and Credit Points

Physical Sciences: Lecture; Elective; Regular; 3.00 points
Chemical Sciences: Lecture; Elective; Regular; 3.00 points

Comments

The course answers the following question: how one makes sense of the system when our knowledge is only partial? 
On May 2st the lecture will be held at Drori Auditorium.

Prerequisites

Statistical Physics I

Restrictions

40

Language of Instruction

English

Attendance and participation

Expected and Recommended

Grade Type

Numerical (out of 100)

Grade Breakdown (in %)

40%
60%

Evaluation Type

Take-home exam

Scheduled date 1

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-
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Estimated Weekly Independent Workload (in hours)

3

Syllabus

Syllabus

Brief reminder: Thermodynamics on one foot. Statistical physics on another foot.

Kinetics and Boltzmann equation.

Dynamics of irreversibility: phase space evolution, Lyapunov exponents, and mixing.

Statistical physics as information theory. The triviality of the second law of thermodynamics.

Mutual information, measurements as communications, data compression.

Applications: from neurons to self-driving cars to quantum computers.

Renormalization group – the right way to forget information.

Learning Outcomes

Upon successful completion of this course, students will be able to:

Use a powerful tool that allows one to study any system, from bacteria to the stock market, when only partial data is available and fluctuations are strong (Universality of approach is achieved by using the language of information theory, equally useful in statistical physics, communication theory, machine learning, etc.).

Reading List

Website

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