研究人员开发了一种新颖的算子学习框架，利用语言模型架构从稀疏数据中重构流场。该方法将稀疏测量视为上下文，将未观测位置视为查询，实现了无网格重构。该方法在包括流体动力学和温度数据在内的各种数据集上展示了具有竞争力的准确性，即使在观测数据少于 10% 的情况下，也凸显了其在科学数据重构方面的潜力。 AI
影响 展示了语言模型在科学数据重构方面的潜力，为工程应用的通用模型指明了方向。
排序理由 该集群包含一篇学术论文，详细介绍了使用语言模型进行科学数据重构的新方法。
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arXiv:2605.23712v1 Announce Type: cross Abstract: 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…
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 fl…