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1
A coarse-grained phase-field crystal model of plastic motion
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A coarse-grained phase-field crystal model of plastic motion

Salvalaglio, Marco ; Angheluta, Luiza ; Huang, Zhi-Feng ; Voigt, Axel ; Elder, Ken R. ; Viñals, Jorge

Journal of the mechanics and physics of solids, 2020-04, Vol.137, p.103856, Article 103856 [Peer Reviewed Journal]

London: Elsevier Ltd

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2
Accelerating deep reinforcement learning strategies of flow control through a multi-environment approach
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Accelerating deep reinforcement learning strategies of flow control through a multi-environment approach

Rabault, Jean ; Kuhnle, Alexander

Physics of fluids (1994), 2019-09, Vol.31 (9) [Peer Reviewed Journal]

Melville: American Institute of Physics

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3
Aerosols at the Poles: An Aerocom Phase II Multi-Model Evaluation
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Aerosols at the Poles: An Aerocom Phase II Multi-Model Evaluation

Sand, Maria ; Bauer, Susanne E. ; Samset, Bjorn H. ; Balkanski, Yves ; Bellouin, Nicolas ; Berntsen, Terje K. ; Bian, Huisheng ; Chin, Mian ; Diehl, Thomas ; Easter, Richard ; Ghan, Steven J. ; Iversen, Trond ; Kirkevag, Alf ; Lamarque, Jean-Francois ; Lin, Guangxing ; Liu, Xiaohong ; Luo, Gan ; Myhre, Gunnar ; van Noije, Twan ; Penner, Joyce E. ; Schulz, Michael ; Seland, Oyvind ; Skeie, Ragnhild B. ; Stier, Philip ; Takemura, Toshihiko ; Tsigaridis, Kostas ; Yu, Fangqun ; Zhang, Kai ; Zhang, Hua

Atmospheric chemistry and physics, 2017-10, Vol.17 (19), p.12197-12218 [Peer Reviewed Journal]

Goddard Space Flight Center: Copernicus Publications

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4
Application of machine learning-based selective sampling to determine BaZrO3 grain boundary structures
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Application of machine learning-based selective sampling to determine BaZrO3 grain boundary structures

Bondevik, Tarjei ; Kuwabara, Akihide ; Løvvik, Ole Martin

Computational materials science, 2019-06, Vol.164, p.57-65 [Peer Reviewed Journal]

Elsevier B.V

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5
Applying deep reinforcement learning to active flow control in weakly turbulent conditions
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Applying deep reinforcement learning to active flow control in weakly turbulent conditions

Ren, Feng ; Rabault, Jean ; Tang, Hui

Physics of fluids (1994), 2021-03, Vol.33 (3) [Peer Reviewed Journal]

Melville: American Institute of Physics

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6
Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control
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Artificial neural networks trained through deep reinforcement learning discover control strategies for active flow control

Rabault, Jean ; Kuchta, Miroslav ; Jensen, Atle ; Réglade, Ulysse ; Cerardi, Nicolas

Journal of fluid mechanics, 2019-04, Vol.865, p.281-302 [Peer Reviewed Journal]

Cambridge, UK: Cambridge University Press

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7
Bayesian inference of atomistic structure in functional materials
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Bayesian inference of atomistic structure in functional materials

Todorović, Milica ; Gutmann, Michael U. ; Corander, Jukka ; Rinke, Patrick

npj computational materials, 2019-03, Vol.5 (1), Article 35 [Peer Reviewed Journal]

London: Nature Publishing Group

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8
Biological aspects of theoretical shell morphology
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Biological aspects of theoretical shell morphology

Savazzi, Enrico

Lethaia, 1990-04, Vol.23 (2), p.195-212 [Peer Reviewed Journal]

Oxford, UK: Scandinavian University Press

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9
Biological responses to physicochemical properties of biomaterial surface
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Biological responses to physicochemical properties of biomaterial surface

Rahmati, Maryam ; Silva, Eduardo A ; Reseland, Janne E ; Heyward, Catherine ; Haugen, Håvard J

Chemical Society reviews, 2020-08, Vol.49 (15), p.5178-5224 [Peer Reviewed Journal]

England: Royal Society of Chemistry

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10
Blastogeny in tabulate corals: case studies using X-ray microtomography
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Blastogeny in tabulate corals: case studies using X-ray microtomography

Zapalski, Mikołaj K. ; Dohnalik, Marek

Lethaia, 2013-04, Vol.46 (2), p.223-231 [Peer Reviewed Journal]

Oslo: Wiley

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