Discovery in Physics
| dc.contributor.editor | Morik, Katharina | |
| dc.contributor.editor | Rhode, Wolfgang | |
| dc.date.accessioned | 2023-01-30T17:09:07Z | |
| dc.date.available | 2023-01-30T17:09:07Z | |
| dc.date.issued | 2022 | |
| dc.identifier | ONIX_20230130_9783110785968_97 | |
| dc.identifier.uri | https://library.oapen.org/handle/20.500.12657/61139 | |
| dc.description.abstract | 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. | |
| dc.language | English | |
| dc.relation.ispartofseries | De Gruyter STEM | |
| dc.subject.classification | thema EDItEUR::P Mathematics and Science::PN Chemistry | en_US |
| dc.subject.classification | thema EDItEUR::U Computing and Information Technology::UM Computer programming / software engineering::UMB Algorithms and data structures | en_US |
| dc.subject.classification | thema EDItEUR::U Computing and Information Technology::UN Databases | en_US |
| dc.subject.classification | thema EDItEUR::U Computing and Information Technology::UT Computer networking and communications | en_US |
| dc.subject.classification | thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence | en_US |
| dc.subject.other | Resource-Constrained Data Analysis | |
| dc.subject.other | Resource-Aware Machine Learning | |
| dc.subject.other | Embedded Systems and Machine Learning | |
| dc.subject.other | Big Data and Machine Learning | |
| dc.subject.other | Artificial Intelligence | |
| dc.subject.other | Highly Distributed Data | |
| dc.subject.other | ML on Small devices Data mining for Ubiquitous System | |
| dc.subject.other | Software Cyber-physical systems | |
| dc.subject.other | Machine learning in high-energy physics | |
| dc.subject.other | Machine learning for knowledge discovery | |
| dc.title | Discovery in Physics | |
| dc.type | book | |
| oapen.identifier.doi | 10.1515/9783110785968 | |
| oapen.relation.isPublishedBy | 2b386f62-fc18-4108-bcf1-ade3ed4cf2f3 | |
| oapen.relation.isbn | 9783110785968 | |
| oapen.relation.isbn | 9783110785951 | |
| oapen.relation.isbn | 9783110786132 | |
| oapen.imprint | De Gruyter | |
| oapen.series.number | Volume 2 | |
| oapen.pages | 349 | |
| oapen.place.publication | Berlin/Boston |
