Mapping surface height dynamics to subsurface flow physics in free-surface turbulent flow using a shallow recurrent decoder
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866914385661788160 |
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| author | Moen, Kristoffer S. Aarnes, Jørgen R. Ellingsen, Simen Å. Kutz, J. Nathan |
| author_facet | Moen, Kristoffer S. Aarnes, Jørgen R. Ellingsen, Simen Å. Kutz, J. Nathan |
| contents | Near-surface turbulent flows beneath a free surface are reconstructed from sparse measurements of the surface height variation, by a novel neural network algorithm known as the {\em SHallow REcurrent Decoder} (SHRED). The reconstruction of turbulent flow fields from limited, partial, or indirect measurements remains a grand challenge in science and engineering. The central goal in such applications is to leverage easy-to-measure proxy variables in order to estimate quantities which have not been, and perhaps cannot in practice be, measured. Specifically, in the application considered here, the aim is to use a sparse number of surface height point measurements of a flow field, or drone video footage of surface features, in order to infer the turbulent flow field beneath the surface. SHRED is a deep learning architecture that learns a delay-coordinate embedding from a few surface height (point) sensors and maps it, via a shallow decoder trained in a compressed basis, to full subsurface fields, enabling fast, robust training from minimal data. We demonstrate the SHRED sensing architecture on two types of turbulent data from recent studies (Aarnes \emph{et al.} J.~Fluid Mech.\ \textbf{1007} A38, 2025 and Babiker \emph{et al.} arXiv:251003732, 2025, respectively): fully resolved DNS data and PIV laboratory data from a turbulent water tank. SHRED is capable of robustly mapping surface height fluctuations to full-state flow fields up to about two integral length scales deep, with as few as three surface measurements. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_06202 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mapping surface height dynamics to subsurface flow physics in free-surface turbulent flow using a shallow recurrent decoder Moen, Kristoffer S. Aarnes, Jørgen R. Ellingsen, Simen Å. Kutz, J. Nathan Fluid Dynamics Near-surface turbulent flows beneath a free surface are reconstructed from sparse measurements of the surface height variation, by a novel neural network algorithm known as the {\em SHallow REcurrent Decoder} (SHRED). The reconstruction of turbulent flow fields from limited, partial, or indirect measurements remains a grand challenge in science and engineering. The central goal in such applications is to leverage easy-to-measure proxy variables in order to estimate quantities which have not been, and perhaps cannot in practice be, measured. Specifically, in the application considered here, the aim is to use a sparse number of surface height point measurements of a flow field, or drone video footage of surface features, in order to infer the turbulent flow field beneath the surface. SHRED is a deep learning architecture that learns a delay-coordinate embedding from a few surface height (point) sensors and maps it, via a shallow decoder trained in a compressed basis, to full subsurface fields, enabling fast, robust training from minimal data. We demonstrate the SHRED sensing architecture on two types of turbulent data from recent studies (Aarnes \emph{et al.} J.~Fluid Mech.\ \textbf{1007} A38, 2025 and Babiker \emph{et al.} arXiv:251003732, 2025, respectively): fully resolved DNS data and PIV laboratory data from a turbulent water tank. SHRED is capable of robustly mapping surface height fluctuations to full-state flow fields up to about two integral length scales deep, with as few as three surface measurements. |
| title | Mapping surface height dynamics to subsurface flow physics in free-surface turbulent flow using a shallow recurrent decoder |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2510.06202 |