prikaz prve stranice dokumenta Building surrogate models of nuclear density functional theory with Gaussian processes and autoencoders
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Building surrogate models of nuclear density functional theory with Gaussian processes and autoencoders
Frontiers in Physics, 10 (2022); 1028370. https://doi.org/10.3389/fphy.2022.1028370
Verriere, Marc; Schunck, Nicolas; Kim, Irene; Marević, Petar; Quinlan, Kevin; Ngo, Michelle N.; Regnier, David; Lasseri, Raphael David

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Building surrogate models of nuclear density functional theory with Gaussian processes and autoencoders - Supplementary Material | Prilog
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Verriere, M., Schunck, N., Kim, I., Marević, P., Quinlan, K., Ngo, M. N. ... Lasseri, R. D. (2022). Building surrogate models of nuclear density functional theory with Gaussian processes and autoencoders. Frontiers in Physics, 10.. doi: 10.3389/fphy.2022.1028370

Verriere, Marc, et al. "Building surrogate models of nuclear density functional theory with Gaussian processes and autoencoders." Frontiers in Physics, vol. 10, 2022. https://doi.org/10.3389/fphy.2022.1028370

Verriere, Marc, Nicolas Schunck, Irene Kim, Petar Marević, Kevin Quinlan, Michelle N. Ngo, David Regnier i Raphael David Lasseri. "Building surrogate models of nuclear density functional theory with Gaussian processes and autoencoders." Frontiers in Physics 10 (2022). https://doi.org/10.3389/fphy.2022.1028370

Verriere, M., et al. (2022) 'Building surrogate models of nuclear density functional theory with Gaussian processes and autoencoders', Frontiers in Physics, 10. doi: 10.3389/fphy.2022.1028370

Verriere M, Schunck N, Kim I, Marević P, Quinlan K, Ngo MN, i sur.. Building surrogate models of nuclear density functional theory with Gaussian processes and autoencoders. Frontiers in Physics [Internet]. 11.2022. [pristupljeno 01.10.2024.];10. doi: 10.3389/fphy.2022.1028370

M. Verriere, et al., "Building surrogate models of nuclear density functional theory with Gaussian processes and autoencoders", Frontiers in Physics, vol. 10, Studeni 2022. [Online]. Dostupno na: https://urn.nsk.hr/urn:nbn:hr:217:231090. [Citirano: 01.10.2024.]

Za citiranje koristite ovu mrežnu adresu: https://urn.nsk.hr/urn:nbn:hr:217:231090
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Podaci o radu
Naslov (engleski)Building surrogate models of nuclear density functional theory with Gaussian processes and autoencoders
AutorMarc Verriere
AutorNicolas Schunck
AutorIrene Kim
AutorPetar Marević
AutorKevin Quinlan
AutorMichelle N. Ngo
AutorDavid Regnier
AutorRaphael David Lasseri
Autorova ustanovaSveučilište u Zagrebu
Prirodoslovno-matematički fakultet
(Fizički odsjek)
Znanstveno / umjetničko
područje, polje i grana		PRIRODNE ZNANOSTI
Fizika
Sažetak (engleski)
From the lightest Hydrogen isotopes up to the recently synthesized Oganesson (Z = 118), it is estimated that as many as about 8, 000 atomic nuclei could exist in nature. Most of these nuclei are too short-lived to be occurring on Earth, but they play an essential role in astrophysical events such as supernova explosions or neutron star mergers that are presumed to be at the origin of most heavy elements in the Universe. Understanding the structure, reactions, and decays of nuclei across the entire chart of nuclides is an enormous challenge because of the experimental difficulties in measuring properties of interest in such fleeting objects and the theoretical and computational issues of simulating stronglyinteracting quantum many-body systems. Nuclear density functional theory (DFT) is a fully microscopic theoretical framework which has the potential of providing such a quantitatively accurate description of nuclear properties for every nucleus in the chart of nuclides. Thanks to highperformance computing facilities, it has already been successfully applied to predict nuclear masses, global patterns of radioactive decay like β or γ decay, and several aspects of the nuclear fission process such as, e.g., spontaneous fission half-lives. Yet, predictive simulations of nuclear spectroscopy—the low-lying excited states and transitions between them—or of nuclear fission, or the quantification of theoretical uncertainties and their propagation to basic or applied nuclear science applications, would require several orders of magnitude more calculations than currently possible. However, most of this computational effort would be spent into generating a suitable basis of DFT wavefunctions. Such a task could potentially be considerably accelerated by borrowing tools from the field of machine learning and artificial intelligence. In this paper, we review different approaches to applying supervised and unsupervised learning techniques to nuclear DFT.
Ključne riječi (engleski)
Jezikengleski
Vrsta publikacijeZnanstveni rad - Izvorni znanstveni rad
Status objaveObjavljen
Vrsta recenzijeRecenziran - međunarodna recenzija
Verzija publikacijeObjavljena verzija rada (izdavačev PDF)
Naslov časopisaFrontiers in Physics
Brojčani podacivol. 10, 1028370
e-ISSN2296-424X
DOIhttps://doi.org/10.3389/fphy.2022.1028370
URN:NBNurn:nbn:hr:217:231090
Datum objave publikacije2022-11
URL dokumentahttps://www.frontiersin.org/articles/10.3389/fphy.2022.1028370/full
Vrsta resursaTekst
Prava pristupaOtvoreni pristup
Uvjeti korištenjaIkona licence Imenovanje 4.0 međunarodna (CC BY 4.0)
Javna napomena© 2022 Verriere, Schunck, Kim, Marević, Quinlan, Ngo, Regnier and Lasseri. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Received 25 August 2022. Accepted 19 October 2022. Published 08 November 2022.
Datum i vrijeme pohrane2024-08-22 20:55:35