流体力学和科学计算的研究者终于有了一个用语言模型做流场重建的新思路——稀疏数据下也能高精度重建，做CFD或实验测量的团队值得关注这个方向。
该研究提出一种基于语言模型架构的算子学习框架，用于从稀疏测量数据重建流场。该方法将流场重建视为序列到序列学习任务，将稀疏测量作为上下文，未观测位置作为查询，以无网格方式学习空间相关性和长程依赖。在四个基准数据集（二维涡街模拟、美国日平均温度、三维血流模拟、三维湍流射流测量）上，即使观测数据少于10%，该方法仍能实现高精度重建。结果表明语言模型可作为科学数据重建的鲁棒可扩展工具，为科学工程基础模型开发提供了新方向。
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) two-dimensional vortex street simulations, (2) daily average temperature data across the contiguous United States, (3) three-dimensional blood flow simulations based on dissipative particle dynamics, and (4) three-dimensional turbulent jet flow measurements obtained via particle tracking velocimetry. Across all cases, our method demonstrates competitive reconstruction accuracy, even with highly incomplete data (less than 10\% observed), and achieves efficient performance. The results highlight the potential of language models as robust and scalable tools for scientific data reconstruction, and suggest a promising direction toward the development of foundation models for scientific and engineering applications.