ViTAE-SL: a vision transformer-based autoencoder and spatial interpolation learner for field reconstruction
Author(s)
Fan, Hongwei
Cheng, Sibo
de Nazelle, Audrey J
Arcucci, Rossella
Type
Journal Article
Abstract
Reliable and accurate reconstruction for large-scale and complex physical fields in real-time from limited observations has been a longstanding challenge. In recent years, sensors have been increasingly deployed in numerous physical systems. However, the locations of these sensors can shift over time, such as with mobile sensors, or when sensors are deployed and removed. These sparse and randomly located sensors further exacerbate the difficulty of reconstructing the physical field. In this paper, we present a new deep learning model called Vision Transformer-based Autoencoder (ViTAE) for reconstructing large-scale and complex fields. The proposed network structure is based on a novel core design: vision transformer encoder and Convolutional Neural Network (CNN) decoder. First, we split a two-dimensional field into patches and developed a vision transformer encoder to transfer patches into latent representations. We then reshape the linear latent representations to patches before concatenation, along with a CNN decoder, to reconstruct the field. The proposed model is tested in four different numerical experiments, using generated synthetic data, spatially distributed PM2.5 data, Computational Fluid Dynamics (CFD) simulation data and National Oceanic and Atmospheric Administration (NOAA) sea surface temperature data. The numerical results highlight the strength of ViTAE-SL compared to Kriging and state-of-the-art deep-learning models with significantly higher reconstruction accuracy, computational efficiency, and robust scaling behavior.
Date Issued
2025-03-01
Date Acceptance
2024-12-03
Citation
Computer Physics Communications, 2025, 308
ISSN
0010-4655
Publisher
Elsevier
Journal / Book Title
Computer Physics Communications
Volume
308
Copyright Statement
Copyright © 2024 Published by Elsevier B.V. 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
Subjects
01 Mathematical Sciences
02 Physical Sciences
08 Information and Computing Sciences
Nuclear & Particles Physics
Publication Status
Published
Article Number
109464
Date Publish Online
2024-12-16