AI RESEARCH
Spatiotemporal System Forecasting with Irregular Time Steps via Masked Autoencoder
arXiv CS.LG
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ArXi:2603.25597v1 Announce Type: new Predicting high-dimensional dynamical systems with irregular time steps presents significant challenges for current data-driven algorithms. These irregularities arise from missing data, sparse observations, or adaptive computational techniques, reducing prediction accuracy. To address these limitations, we propose a novel method: a Physics-Spatiotemporal Masked Autoencoder.