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Discovery in Physics
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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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Keywords
- Algorithms & data structures
- artificial intelligence
- Big Data and Machine Learning
- Chemistry
- Computer networking & communications
- Computer programming / software development
- Computer science
- Computing & information technology
- Databases
- Embedded Systems and Machine Learning
- Highly Distributed Data
- Machine learning for knowledge discovery
- Machine learning in high-energy physics
- Mathematics & science
- ML on Small devices Data mining for Ubiquitous System
- Resource-Aware Machine Learning
- Resource-Constrained Data Analysis
- Software Cyber-physical systems
Links
DOI: 10.1515/9783110785968Editions