Generating Full-field Evolution of Physical Dynamics from Irregular Sparse Observations Article Swipe
Panqi Chen
,
Yifan Sun
,
Lei Cheng
,
Yang Yang
,
Weichang Li
,
Yang Liu
,
Weiqing Liu
,
Jiang Bian
,
Shikai Fang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2505.09284
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2505.09284
Modeling and reconstructing multidimensional physical dynamics from sparse and off-grid observations presents a fundamental challenge in scientific research. Recently, diffusion-based generative modeling shows promising potential for physical simulation. However, current approaches typically operate on on-grid data with preset spatiotemporal resolution, but struggle with the sparsely observed and continuous nature of real-world physical dynamics. To fill the gaps, we present SDIFT, Sequential DIffusion in Functional Tucker space, a novel framework that generates full-field evolution of physical dynamics from irregular sparse observations. SDIFT leverages the functional Tucker model as the latent space representer with proven universal approximation property, and represents observations as latent functions and Tucker core sequences. We then construct a sequential diffusion model with temporally augmented UNet in the functional Tucker space, denoising noise drawn from a Gaussian process to generate the sequence of core tensors. At the posterior sampling stage, we propose a Message-Passing Posterior Sampling mechanism, enabling conditional generation of the entire sequence guided by observations at limited time steps. We validate SDIFT on three physical systems spanning astronomical (supernova explosions, light-year scale), environmental (ocean sound speed fields, kilometer scale), and molecular (organic liquid, millimeter scale) domains, demonstrating significant improvements in both reconstruction accuracy and computational efficiency compared to state-of-the-art approaches.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2505.09284
- https://arxiv.org/pdf/2505.09284
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W4414939131
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https://openalex.org/W4414939131Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2505.09284Digital Object Identifier
- Title
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Generating Full-field Evolution of Physical Dynamics from Irregular Sparse ObservationsWork title
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preprintOpenAlex work type
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enPrimary language
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2025Year of publication
- Publication date
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2025-05-14Full publication date if available
- Authors
-
Panqi Chen, Yifan Sun, Lei Cheng, Yang Yang, Weichang Li, Yang Liu, Weiqing Liu, Jiang Bian, Shikai FangList of authors in order
- Landing page
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https://arxiv.org/abs/2505.09284Publisher landing page
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https://arxiv.org/pdf/2505.09284Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2505.09284Direct OA link when available
- Cited by
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0Total citation count in OpenAlex
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