2026 Lecture on Information Field Theory

Script Recordings Handouts Examinations

Inferring fields (functions over continuous spaces) from finite data is a problem central to signal reconstruction, dynamical systems, and artificial intelligence. Information field theory (IFT) addresses this from a probabilistic perspective by exploiting concepts of statistical physics, field theory, and machine learning. The lecture introduces the key concepts of IFT, its mathematical formalism, its computational methods, its relation to neural field theory and to neural network models, and covers advanced topics like inferring dynamical fields and their dynamical laws empirically. The lecture aims at master students that are theoretically oriented, that want to solve concrete signal reconstruction problems, or that are interested in concepts underlying artificial neural network techniques. It closes with an exam that yields 9 ETCS if performed successfully.

The following topics are planned to be addressed:

The course will be a in person blackboard lecture. Recordings of previous courses on IFT can be found here. Please note that the lecture will deviate from those by aiming for more advanced topics.

There will be two lectures per week
begin: 13.04.2026, end: 14.07.2026

  • Monday, 12:00 - 14:00, Schellingstr. 4, H 030
  • Tuesday, 12:00 - 14:00, Geschw.-Scholl-Pl. 1 (N) / Kleiner Physiksaal (N 020)

Examination: Tuesday, July 14th 2026, 12:00 - 14:00

2026 Tutorials for Information Field Theory

There is one tutorial per week: Begin: 22.04.2026, End: 16.07.2026

  • Wednesday 18:00 - 20:00, Theresienstr. 37 / A 449
    • >

Bonus System

Exercises are optional, but can be used to get bonus points for the exam. The bonus will be 15% of the points which are necessary to achieve a 1.0 in the exam. It will be ensured that top marks are well reachable without the bonus. The bonus system is structured as following:

  • Each week we will hand out work sheets with approximately three problems. Points are assigned to each task and subtask.
  • In a list handed out at the beginning of each tutorial students are asked to denote which which problems they are potentially able to present.
  • In order to get the full bonus for the respective exam (IT or IFT), students need to sign up for at least 60% of all possible achievable points during each part
  • The tutors choose students randomly from the list mentioned earlier.
  • In case the student is not able to properly solve the task, they might lose up to twice the points they could have gained through the correct solution.
  • EXTRA: If the student wants to present voluntarily and do a exceptionally clear and instructive presentation you might get double as many points. If the presentation is not very clear, they get only the regular amount of points.
    (Talk to the tutor before the beginning of the tutorial)

Signal reconstruction with Python, EDV-Zusatzausbildung (SQ1+SQ2, August 31 - September 4, 2026)

One week block-course with exercises and presentation. Signal reconstruction from data will be trained via small programming projects of the students' own choices, followed by presentations of the achievements and experiences. Required is a basic knowledge of Python and a laptop with installations of Python3 as well as the used Python packages "numerical python" (NumPy), "scientific python" (SciPy), and "numerical information field theory" (NIFTy) in their latest announced versions. The course includes several scientific talks which serve as the basis for the following hands-on sessions. For each topic, participants will have the opportunity to actively apply what they have learned. During the final two days of the course, students can work on their own small project to put their acquired skills into practice. The Key Qualification certificate is obtained by a presentation of the student's achievement at the end of the course.
Date: August 31 - September 4, 9:00-17:00
Location: MPI for Astrophysics, Garching
Registration: in July

Currently no seminar is planned