Digicampus
Lecture: Stochastic Processes in Physics - Details
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Lehrveranstaltung wird in Präsenz abgehalten.

General information

Course name Lecture: Stochastic Processes in Physics
Subtitle 2002T
Course number PHM 0293
Semester WS 2023/24
Current number of participants 10
Home institute Lehrstuhl für Theoretische Physik II
Courses type Lecture in category Teaching
First date Monday, 16.10.2023 10:00 - 11:30
Type/Form Master
Participants Students...
• learn about the complexity and diversity of stochastic phenomena of systems
• will understand the relevance of length and time-scale when describing fluctuating systems
• obtain solid expertise in the theoretical techniques required to treat stochastic phenomena, and are able to apply these methods to concrete research problems,
• and will become competent to acquaint themselves with modern scientific questions.
Integrated acquirement of soft skills:
• autonomous working with scientific literature in English,
• improving written and spoken English during lectures and exercises,
• interdisciplinary thinking, and working
Pre-requisites It is recommended to learn the application of some of the theoretical concepts developed in this course by taking it concomitantly or before PHM-0363: Method Course: Applying Theoretical Concepts from Non-equilibrium Statistical Physics.
Online/Digitale Veranstaltung Veranstaltung wird in Präsenz abgehalten.
Hauptunterrichtssprache englisch
Weitere Unterrichtssprache(n) Deutsch
Literaturhinweise • Stochastic Processes in Physics and Chemistry, N. G. Van Kampen, North Holland, ISBN 444529659
• Stochastic Methods: A Handbook for the Natural and Social Sciences, Gardiner, Springer, ISBN
3540707123
• Thinking Probabilistically: Stochastic Processes, Disordered Systems, and Their Applications, Ariel
Amir, Cambridge University Press, ISBN 1108479529
• Statistical Physics of Fields, Mehran Kardar, Cambridge, ISBN 052187341X
ECTS points 6

Rooms and times

No room preference
Monday: 10:00 - 11:30, weekly

Comment/Description

Stochastic processes are mathematical models used to describe and analyze systems that evolve over time in a random or uncertain manner. These processes capture the inherent variability and unpredictability present in many real-world phenomena, such as population growth, movement and interaction of small particles, transport processes inside cells etc. By studying stochastic processes, we gain insights into the statistical behavior, patterns, and trends that emerge from randomness, enabling us to make informed predictions and decisions in a wide range of disciplines, from finance and engineering to biology and physics.