FDTransformer: A firn density prediction framework combining a self-attention transformer network with firn densification physics.

Xueyu Zhang, Lin Liu, Houjun Jiang, Zhicai Luo

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In one paragraph

Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed

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1 citing paper in PubMed.

FDTransformer: A firn density prediction framework combining a self-attention transformer network with firn densification physics.iScience · 2025
Article

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5 · Who and what money

Authors and funding

4 authors.

Xueyu ZhangNational Gravitation Laboratory, MOE Key Laboratory of Fundamental Physical Quantities Measurement, and School of Physics, Huazhong University of Science and Technology, Wuhan 430074, People's Republic of China.

Lin LiuNational Gravitation Laboratory, MOE Key Laboratory of Fundamental Physical Quantities Measurement, and School of Physics, Huazhong University of Science and Technology, Wuhan 430074, People's Republic of China.

Houjun JiangDepartment of Geomatics Science and Engineering, School of Civil Engineering, Anhui Jianzhu University, Hefei 230601, People's Republic of China.

Zhicai LuoNational Gravitation Laboratory, MOE Key Laboratory of Fundamental Physical Quantities Measurement, and School of Physics, Huazhong University of Science and Technology, Wuhan 430074, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate firn density estimation is essential for assessing glacier mass balance. However, the accuracy of existing firn densification models (FDMs) is limited by an incomplete understanding of firn compaction dynamics, particularly as firn structure alters in warming conditions. This study proposes the firn density prediction transformer (FDTransformer), a deep learning framework that combines firn densification physics to improve density estimation. By employing the transformer network with sequential self-attention mechanisms, the FDTransformer learns a nonlinear mapping between physical firn parameters input and observed firn density, enabling physics-to-density sequence transformation. These physically constrained parameters are estimated by applying physics-based FDMs. Evaluated using

Indexed as

environmental monitoringenvironmental scienceglacial landscapesglaciochemistry

Identifiers

PMID41323272

DOI10.1016/j.isci.2025.113869

PMCPMC12661306

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.