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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
Application of a minimal compatible element to incompressible and nearly incompressible continuum mechanics
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Application of a minimal compatible element to incompressible and nearly incompressible continuum mechanics

Burman, Erik ; Christiansen, Snorre H. ; Hansbo, Peter

Computer methods in applied mechanics and engineering, 2020-09, Vol.369, p.113224, Article 113224 [Periódico revisado por pares]

Amsterdam: Elsevier B.V

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6
A statistical mechanics framework for immiscible and incompressible two-phase flow in porous media
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A statistical mechanics framework for immiscible and incompressible two-phase flow in porous media

Hansen, Alex ; Flekkøy, Eirik Grude ; Sinha, Santanu ; Slotte, Per Arne

Advances in water resources, 2022 [Periódico revisado por pares]

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7
Chiral active membranes: Odd mechanics, spontaneous flows, and shape instabilities
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Chiral active membranes: Odd mechanics, spontaneous flows, and shape instabilities

Al-Izzi, Sami C. ; Alexander, Gareth P.

Physical review research, 2023-12, Vol.5 (4), p.043227, Article 043227 [Periódico revisado por pares]

American Physical Society (APS)

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8
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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9
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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10
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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