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Discovery in Physics

Volume 2 covers knowledge discovery in particle and astroparticle physics. Instruments gather petabytes of data and machine learning is used to process the vast amounts of data and to detect relevant examples efficiently. The physical knowledge is encoded in simulations used to train the machine lea...

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Guardado en:
Formato: Online
Lenguaje:inglés
Publicado: De Gruyter 2023
Materias:
Resource-Constrained Data Analysis
Resource-Aware Machine Learning
Embedded Systems and Machine Learning
Big Data and Machine Learning
Artificial Intelligence
Highly Distributed Data
ML on Small devices Data mining for Ubiquitous System
Software Cyber-physical systems
Machine learning in high-energy physics
Machine learning for knowledge discovery
thema EDItEUR::P Mathematics and Science::PN Chemistry
thema EDItEUR::U Computing and Information Technology::UM Computer programming / software engineering::UMB Algorithms and data structures
thema EDItEUR::U Computing and Information Technology::UN Databases
thema EDItEUR::U Computing and Information Technology::UT Computer networking and communications
thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence
Acceso en línea:ONIX_20230130_9783110785968_97
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Sumario:Volume 2 covers knowledge discovery in particle and astroparticle physics. Instruments gather petabytes of data and machine learning is used to process the vast amounts of data and to detect relevant examples efficiently. The physical knowledge is encoded in simulations used to train the machine learning models. The interpretation of the learned models serves to expand the physical knowledge resulting in a cycle of theory enhancement.

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