| Title | Time | Room | Instructor |
|---|---|---|---|
| Topics in High-Dimensional Probability | 22.04.2024 13:15 - 14:30 (Mon) | Central Bldg / O1 / Mondi 2a (I01.O1.008) Central Bldg / O1 / Mondi 2b (I01.O1.008) | Maas, Jan |
| Topics in High-Dimensional Probability (recitation) | 22.04.2024 14:45 - 15:45 (Mon) | Central Bldg / O1 / Mondi 2a (I01.O1.008) Central Bldg / O1 / Mondi 2b (I01.O1.008) | |
| Topics in High-Dimensional Probability | 24.04.2024 13:15 - 14:30 (Wed) | Central Bldg / O1 / Mondi 2a (I01.O1.008) Central Bldg / O1 / Mondi 2b (I01.O1.008) | Maas, Jan |
| Topics in High-Dimensional Probability | 29.04.2024 13:15 - 14:30 (Mon) | Central Bldg / O1 / Mondi 2a (I01.O1.008) Central Bldg / O1 / Mondi 2b (I01.O1.008) | Maas, Jan |
| Topics in High-Dimensional Probability (recitation) | 29.04.2024 14:45 - 15:45 (Mon) | Central Bldg / O1 / Mondi 2a (I01.O1.008) Central Bldg / O1 / Mondi 2b (I01.O1.008) | |
| Topics in High-Dimensional Probability | 06.05.2024 13:15 - 14:30 (Mon) | Central Bldg / O1 / Mondi 2a (I01.O1.008) Central Bldg / O1 / Mondi 2b (I01.O1.008) | Maas, Jan |
| Topics in High-Dimensional Probability (recitation) | 06.05.2024 14:45 - 15:45 (Mon) | Central Bldg / O1 / Mondi 2a (I01.O1.008) Central Bldg / O1 / Mondi 2b (I01.O1.008) | |
| Topics in High-Dimensional Probability | 08.05.2024 13:15 - 14:30 (Wed) | Central Bldg / O1 / Mondi 2a (I01.O1.008) Central Bldg / O1 / Mondi 2b (I01.O1.008) | Maas, Jan |
| Topics in High-Dimensional Probability | 13.05.2024 13:15 - 14:30 (Mon) | Central Bldg / O1 / Mondi 3 (I01.O1.010) | Maas, Jan |
| Topics in High-Dimensional Probability (recitation) | 13.05.2024 14:45 - 15:45 (Mon) | Central Bldg / O1 / Mondi 3 (I01.O1.010) | |
| Topics in High-Dimensional Probability | 15.05.2024 13:15 - 14:30 (Wed) | Central Bldg / O1 / Mondi 2a (I01.O1.008) Central Bldg / O1 / Mondi 2b (I01.O1.008) | Maas, Jan |
| Topics in High-Dimensional Probability | 22.05.2024 13:15 - 14:30 (Wed) | Central Bldg / O1 / Mondi 2a (I01.O1.008) Central Bldg / O1 / Mondi 2b (I01.O1.008) | Maas, Jan |
| Topics in High-Dimensional Probability | 27.05.2024 13:15 - 14:30 (Mon) | Central Bldg / O1 / Mondi 2a (I01.O1.008) Central Bldg / O1 / Mondi 2b (I01.O1.008) | Maas, Jan |
| Topics in High-Dimensional Probability (recitation) | 27.05.2024 14:45 - 15:45 (Mon) | Central Bldg / O1 / Mondi 2a (I01.O1.008) Central Bldg / O1 / Mondi 2b (I01.O1.008) | |
| Topics in High-Dimensional Probability | 29.05.2024 13:15 - 14:30 (Wed) | Central Bldg / O1 / Mondi 2a (I01.O1.008) Central Bldg / O1 / Mondi 2b (I01.O1.008) | Maas, Jan |
| Topics in High-Dimensional Probability | 03.06.2024 13:15 - 14:30 (Mon) | Central Bldg / O1 / Mondi 2a (I01.O1.008) Central Bldg / O1 / Mondi 2b (I01.O1.008) | Maas, Jan |
| Topics in High-Dimensional Probability (recitation) | 03.06.2024 14:45 - 15:45 (Mon) | Central Bldg / O1 / Mondi 2a (I01.O1.008) Central Bldg / O1 / Mondi 2b (I01.O1.008) | |
| Topics in High-Dimensional Probability | 05.06.2024 13:15 - 14:30 (Wed) | Central Bldg / O1 / Mondi 2a (I01.O1.008) Central Bldg / O1 / Mondi 2b (I01.O1.008) | Maas, Jan |
Description:
The course deals with the long-term behaviour of stochastic processes and related geometric and functional inequalities. Possible topics include logarithmic Sobolev inequalities, semigroup methods, optimal transport, Schrödinger bridges, stochastic localisation, and the Polchinski renormalisation flow.
References:
Roland Bauerschmidt, Thierry Bodineau, Benoit Dagallier
Stochastic dynamics and the Polchinski equation: an introduction ( https://arxiv.org/abs/2307.07619 )
Ronen Eldan
Analysis of high-dimensional distributions using pathwise methods ( https://www.wisdom.weizmann.ac.il/~ronene/files/Pathwise.pdf )
Capacity:
2/30
Course Code:
C_MAT-4010_S24
Course instructor(s):
Jan Maas
Main Contact:
Jan Maas
Course type:
Taught course
Course level:
Advanced/specialized
Primary Track:
Mathematics
Course format:
On campus
Classroom requirements:
Blackboard
Window
Duration:
Half semester
ECTS:
3
Semester:
Spring 2
Target audience:
Graduate students in mathematics and related fields.
Prerequisites:
Knowledge of calculus and elementary probability is required. Knowledge of measure theory is helpful.
Teaching format:
Classroom lectures and homework exercises
Assessment form(s):
Pass/fail based on homework exercises
Grading scheme:
Pass/fail
Course Category:
Credit Course
Academic Year:
AY 2023/24