Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Felix Brockherde"'
Publikováno v:
Nature Communications, Vol 8, Iss 1, Pp 1-10 (2017)
Machine learning allows electronic structure calculations to access larger system sizes and, in dynamical simulations, longer time scales. Here, the authors perform such a simulation using a machine-learned density functional that avoids direct solut
Externí odkaz:
https://doaj.org/article/5eb66063f9824f3e9aa4606612d626ee
Autor:
Sebastian Houben, Stephanie Abrecht, Maram Akila, Andreas Bär, Felix Brockherde, Patrick Feifel, Tim Fingscheidt, Sujan Sai Gannamaneni, Seyed Eghbal Ghobadi, Ahmed Hammam, Anselm Haselhoff, Felix Hauser, Christian Heinzemann, Marco Hoffmann, Nikhil Kapoor, Falk Kappel, Marvin Klingner, Jan Kronenberger, Fabian Küppers, Jonas Löhdefink, Michael Mlynarski, Michael Mock, Firas Mualla, Svetlana Pavlitskaya, Maximilian Poretschkin, Alexander Pohl, Varun Ravi-Kumar, Julia Rosenzweig, Matthias Rottmann, Stefan Rüping, Timo Sämann, Jan David Schneider, Elena Schulz, Gesina Schwalbe, Joachim Sicking, Toshika Srivastava, Serin Varghese, Michael Weber, Sebastian Wirkert, Tim Wirtz, Matthias Woehrle
Publikováno v:
Deep Neural Networks and Data for Automated Driving ISBN: 9783031012327
Fingscheidt, Gottschalk et al. (Hg.): Deep Neural Networks and Data for Automated Driving. Robustness, Uncertainty Quantification, and Insights Towards Safety
Fingscheidt, Gottschalk et al. (Hg.): Deep Neural Networks and Data for Automated Driving. Robustness, Uncertainty Quantification, and Insights Towards Safety
The use of deep neural networks (DNNs) in safety-critical applications like mobile health and autonomous driving is challenging due to numerous model-inherent shortcomings. These shortcomings are diverse and range from a lack of generalization over i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a97320425a5b7fac20b0272cdc9fe19b
https://doi.org/10.1007/978-3-031-01233-4_1
https://doi.org/10.1007/978-3-031-01233-4_1
Publikováno v:
Nature Communications, Vol 8, Iss 1, Pp 1-10 (2017)
Brockherde, F; Vogt, L; Li, L; Tuckerman, ME; Burke, K; & Mueller, K-R. (2017). Bypassing the Kohn-Sham equations with machine learning. NATURE COMMUNICATIONS, 8, 872. doi: 10.1038/s41467-017-00839-3. UC Irvine: Retrieved from: http://www.escholarship.org/uc/item/54t1815h
Nature Communications
Brockherde, F; Vogt, L; Li, L; Tuckerman, ME; Burke, K; & Mueller, K-R. (2017). Bypassing the Kohn-Sham equations with machine learning. NATURE COMMUNICATIONS, 8, 872. doi: 10.1038/s41467-017-00839-3. UC Irvine: Retrieved from: http://www.escholarship.org/uc/item/54t1815h
Nature Communications
Last year, at least 30,000 scientific papers used the Kohn–Sham scheme of density functional theory to solve electronic structure problems in a wide variety of scientific fields. Machine learning holds the promise of learning the energy functional