Latent attention on masked patches for flow reconstruction
File(s)
Accepted version
Author(s)
Eze, Ben
Magri, Luca
Novoa, Andrea
Type
Conference Paper
Abstract
Vision transformers have shown outstanding performance in image generation, yet their adoption in fluid dynamics remains limited. We introduce the Latent Attention on Masked Patches (LAMP) model, an interpretable regression-based modified vision transformer designed for masked flow reconstruction. LAMP follows a three-fold strategy: (i) partition of each flow snapshot into patches, (ii) patch-wise dimensionality reduction via proper orthogonal decomposition, and (iii) reconstruction of the full field from a masked input using a single-layer transformer trained via closed-form linear regression. We test the method on two canonical 2D unsteady wakes: a laminar wake past a bluff body, and a chaotic wake past two cylinders. On the laminar case, LAMP accurately reconstructs the full flow field from a 90%-masked and noisy input, across signal-to-noise ratios between 10 and 30 dB. Further, the learned attention matrix yields interpretable multi-fidelity optimal sensor-placement maps. LAMP’s performance on the chaotic wake is limited, but outperforms other regression methods such as gappy POD. The modularity of the framework, however, naturally accommodates nonlinear compression and deep attention blocks, thereby providing an efficient baseline for nonlinear, high-dimensional masked flow reconstruction.
Date Issued
2026-06-29
Date Acceptance
2026-04-10
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2026, 16788, pp.181-188
ISBN
978-3-032-29914-7
ISSN
0302-9743
Publisher
Springer
Start Page
181
End Page
188
Journal / Book Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
16788
Copyright Statement
© 2026 The Author(s), under exclusive license to Springer Nature Switzerland AG. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Source
International Conference on Computational Science (ICCS 2026)
Publication Status
Published
Start Date
2026-06-29
Finish Date
2026-07-01
Coverage Spatial
Hamburg, Germany
Date Publish Online
2026-06-29