Author

Phillip Si

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#machine learning Preprint Oct 2026

LD-EnFF: Latent-Dynamics Ensemble Flow Filtering for Data Assimilation with Sparse Observations

Data assimilation combines model forecasts with noisy, incomplete observations to estimate the evolving state of a dynamical system. Existing methods face two compounding challenges: high-dimensional nonlinear dynamics make repeated forward simulation computationally expensive, while sparse observations provide limited...

Zi-Yu Tian, Kai-Cheng Shen, Wen-Bo Hao et al. · 0 citations

#artificial intelligence Preprint Sep 2026

C-STRIDE: An Observation-Driven AI Digital Twin for Predicting Basin-Wide Flood Fields from Sparse Stream-Gauge Histories

Emergency managers need to know where floodwater is, how deep it is, and how it will change over the coming hours across an entire river basin. During a flood, however, real-time measurements come from only a handful of stream gauges, and high-resolution hydrodynamic models are too costly to rerun each time new data ar...

Yan-Jie Tong, Phillip Si, Yuan Qiu et al. · 0 citations