StreamPhy: Streaming Inference of High-Dimensional Physical Dynamics via State Space Models

• URL: http://arxiv.org/abs/2605.07384v2
• Date: Mon, 11 May 2026 13:29:48 GMT
• Title: StreamPhy: Streaming Inference of High-Dimensional Physical Dynamics via State Space Models
• Abstract summary: StreamPhy is an end-to-end framework that enables efficient streaming inference of full-field physical dynamics from irregular measurements.<n>We show that StreamPhy consistently outperforms state-of-the-art baselines, with at least 48% improvement in accuracy and up to 20--100X faster inference than diffusion-based methods.
• Score: 18.54324737751976
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. We propose StreamPhy, an end-to-end framework that enables efficient and accurate streaming inference of full-field physical dynamics from incoming irregular sparse measurements. The framework integrates a data-adaptive observation encoder that is robust to arbitrary observation patterns, a structured state-space model that supports memory-efficient online updates across irregular time intervals, and an expressive Functional Tensor Feature-wise Linear Modulation (FT-FiLM) decoder for continuous-field generation. We prove that FT-FiLM is more expressive than the functional Tucker model, admitting a richer function class for handling complex dynamics. Experiments on three representative physical systems under challenging sampling patterns show that StreamPhy consistently outperforms state-of-the-art baselines, with at least 48\% improvement in accuracy and up to 20--100X faster inference than diffusion-based methods.

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