AI RESEARCH
StreamPhy: Streaming Inference of High-Dimensional Physical Dynamics via State Space Models
arXiv CS.LG
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ArXi:2605.07384v1 Announce Type: new Inferring the evolution of high-dimensional and multi-modal (e.g., spatio-temporal) physical fields from irregular sparse measurements in real time is a fundamental challenge in science and engineering. Existing approaches, including diffusion-based generative models and functional tensor methods, typically operate in offline settings, depend on full temporal observations, or incur substantial inference cost.