Reconstructing full spatio-temporal dynamics from sparse observations in both space and time remains a central challenge in complex systems, as measurements can be spatially incomplete and can be also limited to narrow temporal windows. Yet approximating the complete spatio-temporal trajectory is essential for mechanistic insight and understanding, model calibration, and operational decision-making. We introduce LAPIS-SHRED (LAtent Phase Inference from Short time sequence using SHallow REcurrent Decoders), a modular architecture that reconstructs and/or forecasts complete spatiotemporal dynamics from sparse sensor observations confined to short temporal windows. LAPIS-SHRED operates through a three-stage pipeline: (i) a SHRED model is pre-trained entirely on simulation data to map sensor time-histories into a structured latent space, (ii) a temporal sequence model, trained on simulation-derived latent trajectories, learns to propagate latent states forward or backward in time to span unobserved temporal regions from short observational time windows, and (iii) at deployment, only a short observation window of hyper-sparse sensor measurements from the true system is provided, from which the frozen SHRED model and the temporal model jointly reconstruct or forecast the complete spatiotemporal trajectory. The framework supports bidirectional inference, inherits data assimilation and multiscale reconstruction capabilities from its modular structure, and accommodates extreme observational constraints including single-frame terminal inputs. We evaluate LAPIS-SHRED on six experiments spanning complex spatio-temporal physics: turbulent flows, multiscale propulsion physics, volatile combustion transients, and satellite-derived environmental fields, highlighting a lightweight, modular architecture suited for operational settings where observation is constrained by physical or logistical limitations.

翻译：从空间和时间上的稀疏观测中重建完整的时空动力学仍是复杂系统中的核心挑战，因为测量在空间上可能不完整，且局限于狭窄的时间窗口。然而，近似完整的时空轨迹对于机制洞察理解、模型校准及操作决策至关重要。我们提出LAPIS-SHRED（基于短时间序列的浅层循环解码器潜在阶段推断），这是一种模块化架构，可从局限于短时间窗口的稀疏传感器观测中重建和/或预测完整的时空动力学。LAPIS-SHRED通过三阶段流水线运行：（i）完全基于仿真数据预训练SHRED模型，将传感器时间历程映射到结构化潜在空间；（ii）基于仿真导出的潜在轨迹训练时序模型，学习在时间上前向或后向传播潜在状态，以从短观测时间窗口覆盖未观测的时间区域；（iii）部署时，仅需提供真实系统中超稀疏传感器测量的短观测窗口，冻结的SHRED模型与时序模型共同重建或预测完整的时空轨迹。该框架支持双向推断，通过模块化结构继承数据同化与多尺度重建能力，并适应极端观测约束（包括单帧终端输入）。我们在六项涵盖复杂时空物理的实验上评估LAPIS-SHRED：湍流、多尺度推进物理、挥发性燃烧瞬态及卫星导出的环境场，突显了轻量级模块化架构在受物理或后勤限制的观测场景中的适用性。