Robust deep density models for high-energy physics and solar physics (RODEM)
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DOI der Originalpublikation
Projekttyp
angewandte Forschung
Projektbeginn
2021
Projektende
2024
Projektstatus
laufend
Projektkontakt
Projektmanager:in
Beteiligte
Beschreibung
Zusammenfassung
RODEM is a SNSF/SINERGIA project fostering cooperation between high energy physicists, solar physicists and experts in machine learning in Switzerland to advance research methodologies in both fields. During the last decade, the amount of data available to scientists has increased enormously. New infrastructures, such as the Large Hadron Collider (LHC), and a new generation of solar observatories, such as the Solar Dynamics Observatory (SDO), produce data on a scale that cannot be exploited to their full extent with existing methods. Simultaneously, data science has experienced real game changing breakthroughs in the past years. In particular, deep learning methods have shown the potential of data driven approaches compared to traditional algorithmic approaches. The following questions will shape research stragegies.
Can data driven methods support us in unraveling new physics?
Can physics support us in making better deep learning models?
Now is the time to unite our experience in both physics and machine learning to deeply dive into the many challenges of this combination. The rewards will undoubtedly bring both domains forward, resulting in better forecasting tools, generative models and anomaly detectors to be applied in the fields of High Energy Physics and Solar Physics.
Link
Während FHNW Zugehörigkeit erstellt
Yes
Zukunftsfelder FHNW
Hochschule
Hochschule für Technik
Institut
Institut für Data Science
Finanziert durch
Schweizerischer Nationalfonds (SNF)
Projektpartner
Université de Genève
Auftraggeberschaft
SAP Referenz
Schlagwörter
Machine Learning
Fachgebiet (DDC)
530 - Physik