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Ergebnis der Suche nach: "Machine Learning"
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Artikel 25871 Probing dark QCD sector through the Higgs portal with machine learning at the LHC
Enthalten in Journal of high energy physics Bd. 2023, 28.8.2023, Nr. 8, date:8.2023: 1-44
Online Ressource
Artikel 25872 Probing nuclear quantum effects in electrocatalysis via a machine-learning enhanced grand canonical constant potential approach
Enthalten in Nature Communications Bd. 16, 16.4.2025, Nr. 1, date:12.2025: 1-13
Online Ressource
Artikel 25873 Probing out-of-distribution generalization in machine learning for materials
Enthalten in Communications materials Bd. 6, 11.1.2025, Nr. 1, date:12.2025: 1-10
Online Ressource
Artikel 25874 Probing the Rheological Properties of Liquids Under Conditions of Elastohydrodynamic Lubrication Using Simulations and Machine Learning
Enthalten in Tribology letters Bd. 69, 27.5.2021, Nr. 3, date:9.2021: 1-19
Online Ressource
Artikel 25875 Probing the transition from dislocation jamming to pinning by machine learning
Enthalten in Materials theory Bd. 4, 9.10.2020, Nr. 1, date:12.2020: 1-16
Online Ressource
Artikel 25876 Probing Vegetation, Climatic Data, and Machine Learning for Agricultural Planning and Climate Action: A Case Study from North India
Enthalten in Remote sensing in earth systems sciences Bd. 8, 30.12.2024, Nr. 1, date:3.2025: 213-231
Online Ressource
Artikel 25877 Procedure Model for the Use of Machine Learning
Enthalten in ATZ worldwide Bd. 126, 23.2.2024, Nr. 2-3, date:2.2024: 48-52
Online Ressource
Artikel 25878 Process Design of Laser Powder Bed Fusion of Stainless Steel Using a Gaussian Process-Based Machine Learning Model
Enthalten in JOM Bd. 72, 23.9.2019, Nr. 1, date:1.2020: 420-428
Online Ressource
Artikel 25879 Process–Material–Performance Trade-off Exploration of Materials Sintering with Machine Learning Models
Enthalten in Integrating materials and manufacturing innovation Bd. 13, 13.11.2024, Nr. 4, date:12.2024: 927-941
Online Ressource
Artikel 25880 Process parameter optimisation for selective laser melting of AlSi10Mg-316L multi-materials using machine learning method
Enthalten in The international journal of advanced manufacturing technology Bd. 129, 21.10.2023, Nr. 7-8, date:12.2023: 3093-3108
Online Ressource


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