Author

Siming Liang

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Preprint Sep 2026

From Variational Optimization to Flow-Based Transport: Posterior-Geometry Regularization for Ill-Conditioned Data Assimilation

Variational data assimilation computes a Bayesian update through optimization on a fixed posterior geometry, whose conditioning is determined by the prior covariance and the observation operator. In strongly anisotropic, weakly observed, or highly informative regimes, this geometry can become severely ill conditioned,...

Si-Ming Liang, Feng Bao, Hristo G. Chipilski et al. · 0 citations

#generative ai Preprint Sep 2026

Score-Based Generative Data Assimilation for Integrating Aggregated Surveillance Data into Agent-Based Models in Epidemic Tracking

Reliable epidemic monitoring often requires inferring regional infection burden and transmission heterogeneity from noisy, spatially aggregated, and potentially sparse surveillance data. Agent-based models (ABMs) are attractive for this task because they represent individual behavior, contact heterogeneity, and localiz...

Siming Liang, Jacob Hauck, Minglei Yang et al. · 0 citations

#artificial intelligence Apr 2026

Global Attention with Linear Complexity for Exascale Generative Data Assimilation in Earth System Prediction

STORM is introduced, a one-stage generative AI framework that reformulates DA as diffusion-based Bayesian posterior sampling, replacing online PDE ensemble forecasts with scalable AI inference, enabling high-resolution, long-context Earth modeling and demonstrating improved accuracy.

Xiao Wang, Ze-Zhong Zhang, Isaac Lyngaas et al. · arXiv.org · 1 citation