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1
A review on deep reinforcement learning for fluid mechanics
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A review on deep reinforcement learning for fluid mechanics

Garnier, Paul ; Viquerat, Jonathan ; Rabault, Jean ; Larcher, Aurélien ; Kuhnle, Alexander ; Hachem, Elie

Computers & fluids, 2021-07, Vol.225, p.104973, Article 104973 [Periódico revisado por pares]

Amsterdam: Elsevier Ltd

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2
Envisioning faults beyond the framework of fracture mechanics
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Envisioning faults beyond the framework of fracture mechanics

Torabi, Anita ; Rudnicki, John ; Alaei, Behzad ; Buscarnera, Giuseppe

Earth-science reviews, 2023-03, Vol.238, p.104358, Article 104358 [Periódico revisado por pares]

Elsevier B.V

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3
Deformation imaging and rotational mechanics in neonates: a guide to image acquisition, measurement, interpretation, and reference values
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Deformation imaging and rotational mechanics in neonates: a guide to image acquisition, measurement, interpretation, and reference values

El-Khuffash, Afif ; Schubert, Ulf ; Levy, Philip T ; Nestaas, Eirik ; de Boode, Willem P

Pediatric research, 2018-07, Vol.84 (Suppl 1), p.30-45 [Periódico revisado por pares]

United States: Nature Publishing Group

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4
Fiber reinforced hydrated networks recapitulate the poroelastic mechanics of articular cartilage
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Fiber reinforced hydrated networks recapitulate the poroelastic mechanics of articular cartilage

Moore, A.C. ; Hennessy, M.G. ; Nogueira, L.P. ; Franks, S.J. ; Taffetani, M. ; Seong, H. ; Kang, Y.K. ; Tan, W.S. ; Miklosic, G. ; El Laham, R. ; Zhou, K. ; Zharova, L. ; King, J.R. ; Wagner, B. ; Haugen, H.J. ; Münch, A. ; Stevens, M.M.

Acta biomaterialia, 2023-09, Vol.167, p.69-82 [Periódico revisado por pares]

England: Elsevier Ltd

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5
Granular porous landslide tsunami modelling - the 2014 Lake Askja flank collapse
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Granular porous landslide tsunami modelling - the 2014 Lake Askja flank collapse

Rauter, Matthias ; Viroulet, Sylvain ; Gylfadóttir, Sigríður Sif ; Fellin, Wolfgang ; Løvholt, Finn

Nature communications, 2022-02, Vol.13 (1), p.678-678, Article 678 [Periódico revisado por pares]

England: Nature Publishing Group

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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 [Periódico revisado por pares]

Cambridge, UK: Cambridge University Press

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7
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) [Periódico revisado por pares]

Melville: American Institute of Physics

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8
A radicle solution: morphology and biomechanics of the Eucalyptocrinites ‘root’ system
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A radicle solution: morphology and biomechanics of the Eucalyptocrinites ‘root’ system

Plotnick, Roy E. ; Ebey, Chris M. ; Zinga, Anna

Lethaia, 2016-01, Vol.49 (1), p.130-144 [Periódico revisado por pares]

Oslo: Wiley

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9
Asymptotic regimes in elastohydrodynamic and stochastic leveling on a viscous film
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Asymptotic regimes in elastohydrodynamic and stochastic leveling on a viscous film

Pedersen, Christian ; Niven, John F. ; Salez, Thomas ; Dalnoki-Veress, Kari ; Carlson, Andreas

Physical review fluids, 2019-12, Vol.4 (12), Article 124003 [Periódico revisado por pares]

American Physical Society

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10
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) [Periódico revisado por pares]

Melville: American Institute of Physics

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