Operator Learning for Reconstructing Flow Fields from Sparse Measurements: a Language Model Approach
Reconstructing flow fields from sparse measurements is a fundamental problem in fluid mechanics with broad implications for modeling, control, and design. In this work, we propose a novel operator learning framework that leverages the architecture of language models to perform flow reconstruction in a mesh-free manner. We reformulate flow field reconstruction as a sequence-to-sequence learning task, where sparse measurements are treated as context and unobserved locations as queries. Our model learns to reconstruct the full flow field from sparse inputs, effectively capturing spatial correlations and long-range dependencies. We evaluate the proposed approach on four benchmark datasets: (1...
| Authors | Qian Zhang, George Em Karniadakis |
|---|---|
| Primary category | cs.CE |
| Published | 22 May 2026 |
| Last updated at source | 22 May 2026 |
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