[2604.01216] LAtent Phase Inference from Short time sequences using SHallow REcurrent Decoders (LAPIS-SHRED)

Computer Science > Machine Learning arXiv:2604.01216 (cs) [Submitted on 1 Apr 2026] Title:LAtent Phase Inference from Short time sequences using SHallow REcurrent Decoders (LAPIS-SHRED) Authors:Yuxuan Bao, Xingyue Zhang, J. Nathan Kutz View a PDF of the paper titled LAtent Phase Inference from Short time sequences using SHallow REcurrent Decoders (LAPIS-SHRED), by Yuxuan Bao and 2 other authors View PDF HTML (experimental) Abstract: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 wind...