AIME21

Session

AIME21 12. Nov. Morning

Nov 12, 2021, 9:00 AM

1. Machine Learning for Simulation in High Energy Physics

Dalila Salamani (CERN)

11/12/21, 9:00 AM

Track #2

Lecture

The simulation of the passage of particles through the detectors of High Energy Physics (HEP) experiments is a core component of any physics analysis. However, a detailed and accurate simulation of the detector response using the Geant4 toolkit is a time and CPU consuming process, making it a challenge to gather enough statistics. This is especially problematic for the upcoming High Luminosity...

18. Using high-level C++ for HEP Data Processing on Accelerators

Attila Krasznahorkay (CERN)

11/12/21, 9:45 AM

Track #2

Lecture

19. Machine Learning Applications in Astronomy

Róbert Szabó (Konkoly Observatory, ELKH CSFK, Research Centre for Astronomy and Earth Sciences, Budapest)

11/12/21, 10:45 AM

Track #2

Lecture

3. Deep learning-based microscopy automation applications

Krisztián Koós (ELKH SZBK)

11/12/21, 11:10 AM

Track #2

Lecture

Deep learning is widely used in microscopy applications, mainly for processing the generated data, e.g. image segmentation and classification. Real-time applications are less common due to the research focus to develop the best possible method for a given problem and the time it requires to engineer. However, such approaches help in automating monotonous manual processes, they standardize the...

20. Machine Learning on Graphs and Quantum Graphs for Particle Tracking and Industrial Applications

Dobos Dániel (GluoNNet), Richard Forster (CERN, Switzerland)

11/12/21, 11:35 AM

Track #2

Lecture

6. Utilise network science in multidimensional system modelling and analysis of complex manufacturing system

László Nagy (PhD student)

11/12/21, 12:00 PM

Track #2

Lecture

One of the central questions of the digital transformation is how Industry 4.0 can be utilised to fulfil all the production system requirements while following state-of-the-art developments. Effective information management is critical for the improvement of manufacturing processes. This research high- lights that ontologies are suitable for manufacturing management and recommends the...