English(EN) Latent dynamics for reconstructing fine-scale PDE trajectories from coarse observations

新框架从粗粒度数据重建精细尺度偏微分方程轨迹

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-28 04:00

研究人员开发了结构化潜在动力学（SLD），这是一个旨在从空间和时间上降采样模拟中重建演化场的高分辨率轨迹的新框架。SLD通过学习到的校正来增强时间插值，并使用此精炼序列来指导粗网格上的隐藏状态。该框架结合了学习到的空间项和Haar小波合成分支来生成高分辨率场，在减少各种复杂系统上的重建误差方面表现优于基线方法。 AI

影响该框架通过从较少的数据中进行详细重建，可以提高科学模拟的效率和准确性。

排序理由学术论文，详细介绍了从粗粒度观测重建精细尺度数据的计算框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Chenhao Si, Zishan Qian, Shing-Tung Yau, Rongling Wu, Ming Yan · 2026-10-28 04:00

从粗粒度观测重构精细尺度偏微分方程轨迹的潜在动力学

arXiv:2610.33722v1 Announce Type: new Abstract: Coarse PDE observations omit spatial detail and intermediate states of evolving fields. We present Structured Latent Dynamics (SLD), a framework that reconstructs high-resolution trajectories from spatially and temporally downsample…

本文如何被排名

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从粗粒度观测重构精细尺度偏微分方程轨迹的潜在动力学

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