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ODSL GenAI Days Workshop

23–25 Sept 2024
Europe/Berlin timezone

Contribution List

18 / 18
7. Welcome / Intro to GenAI
Nicole Hartman (TUM)
23/09/2024, 14:00
8. Lecture: Discrete Normalizing Flows
Annalena Kofler
23/09/2024, 14:15
11. Hands On
Annalena Kofler
23/09/2024, 15:30
25. Integration of Augmented Reality and LLMs to Teach ALICE Detector
Atakan Coban (Ludwig Maximilian University - Physics Education Chair)
24/09/2024, 10:00

Teaching complex physics topics such as the quark-gluon plasma investigated by CERN's ALICE detector is extremely important in nurturing future scientists. In this project, we aim to enhance learning by integrating the ChatGPT language model into an Augmented Reality (AR) environment that comprehensively addresses the ALICE detector.

Within this scope, an AR application suitable for use...
26. Flow Annealed Importance Sampling Bootstrap meets Differentiable Particle Physics
Annalena Kofler
24/09/2024, 10:20

High-energy physics requires the generation of large numbers of simulated data
samples from complex but analytically tractable distributions called matrix elements. Surrogate models, such as normalizing flows, are gaining popularity for this task due to their computational efficiency. We adopt an approach based on Flow Annealed importance sampling Bootstrap (FAB) that evaluates the...
27. Maria simulator for CMB experiments (12'+3')
Tony Mroczkowski (ESO)
24/09/2024, 10:40
17. Lecture: Continuous Normalizing Flows + Flow Matching
24/09/2024, 11:15
15. Hands On
Nicole Hartman (TUM)
24/09/2024, 14:00
18. Diffusion Models: Lecture
Eva Sextl (USM @ LMU), Eva Sextl (USM @ LMU)
25/09/2024, 10:00
29. Closeout
Nicole Hartman (TUM)
25/09/2024, 10:55

How to stay connected
19. Diffusion Models: Tutorial
25/09/2024, 11:10
28. Closeout
Nicole Hartman (TUM)
25/09/2024, 12:25
16. Applications

Based on abstract submissions
24. Controlling dynamics of stochastic systems with deep reinforcement learning (12'+3')
Ruslan Mukhamadiarov (LMU)

A properly designed controller helps to stabilize the system against environmental perturbations and improve the quality of experiment measurements. The task of tuning a controller to optimize its performance is the subject of optimal control theory and with development of deep reinforcement learning — a powerful new learning approach that employs artificial neural networks — the interest to...
22. Flow Annealed Importance Sampling Bootstrap meets Differentiable Particle Physics
Annalena Kofler

High-energy physics requires the generation of large numbers of simulated data
samples from complex but analytically tractable distributions called matrix elements. Surrogate models, such as normalizing flows, are gaining popularity for this task due to their computational efficiency. We adopt an approach based on Flow Annealed importance sampling Bootstrap (FAB) that evaluates the...
20. Informal discussions
Q&A

Open space (optional) to talk about new or ongoing ORIGINS problems you'd like to apply GenAI to
23. Integration of Augmented Reality and LLMs to Teach ALICE Detector
Atakan Coban (Ludwig Maximilian University - Physics Education Chair)

Teaching complex physics topics such as the quark-gluon plasma investigated by CERN's ALICE detector is extremely important in nurturing future scientists. In this project, we aim to enhance learning by integrating the ChatGPT language model into an Augmented Reality (AR) environment that comprehensively addresses the ALICE detector.

Within this scope, an AR application suitable for use...
14. Lecture
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