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Cited by
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This article has been cited by the following publications. This list is generated based on data provided by Crossref.
Martin-Linares, Cristina P. Psarellis, Yorgos M. Karapetsas, George Koronaki, Eleni D. and Kevrekidis, Ioannis G. 2023. Physics-agnostic and physics-infused machine learning for thin films flows: modelling, and predictions from small data. Journal of Fluid Mechanics, Vol. 975, Issue. ,
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Rezaeiravesh, S. Mukha, T. and Schlatter, P. 2024. Progress in Turbulence X. Vol. 404, Issue. , p. 297.
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Jiang, Yu Liang, Yuanfei and Yuan, Xue-Feng 2024. Super-resolution reconstruction of turbulence for Newtonian and viscoelastic fluids with a physical constraint. Physics of Fluids, Vol. 36, Issue. 10,
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Kim, Minji O’Connor, Kevin Pipiras, Vladas and Sapsis, Themistoklis 2025. Sampling Low-Fidelity Outputs for Estimation of High-Fidelity Density and Its Tails. SIAM/ASA Journal on Uncertainty Quantification, Vol. 13, Issue. 1, p. 30.
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Efficient prediction of turbulent flow quantities using a Bayesian hierarchical multifidelity model
  • Volume 964
  • S. Rezaeiravesh (a1) (a2) (a3), T. Mukha (a2) (a3) and P. Schlatter (a2) (a3) (a4)
  • DOI: https://doi.org/10.1017/jfm.2023.327
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Efficient prediction of turbulent flow quantities using a Bayesian hierarchical multifidelity model
  • Volume 964
  • S. Rezaeiravesh (a1) (a2) (a3), T. Mukha (a2) (a3) and P. Schlatter (a2) (a3) (a4)
  • DOI: https://doi.org/10.1017/jfm.2023.327
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Efficient prediction of turbulent flow quantities using a Bayesian hierarchical multifidelity model
  • Volume 964
  • S. Rezaeiravesh (a1) (a2) (a3), T. Mukha (a2) (a3) and P. Schlatter (a2) (a3) (a4)
  • DOI: https://doi.org/10.1017/jfm.2023.327
Available formats Please select a format to save.
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