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R. Maulik

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#machine learning Preprint Sep 2026

Generative Atmospheric Super-Resolution from Heterogeneous In Situ Observations through Composable Interfaces

This work forms this reconstruction problem as generative atmospheric super-resolution and introduces composable observation interfaces for conditioning a single pretrained 13-variable atmospheric diffusion model without retraining the underlying model.

Yang Xu, Dibyajyoti Chakraborty, Hai-Wen Guan et al. · 0 citations
#artificial intelligence Review Sep 2026

Hapi: A Multivariable Land-Surface Transformer for Medium-Range Hydrological Forecasting at Continental Scale

Accurate flood forecasts several days in advance are essential for flood control, water-resource management, and emergency response. A central challenge is to produce high-resolution forecasts across continental domains where hydrological behavior varies widely from place to place. We developed Hapi, a U-Net Swin Trans...

Hong Zhang, John K. Hutchison, R. Kotamarthi et al. · 0 citations

A Weak Penalty Neural ODE for Learning Chaotic Dynamics from Noisy Time Series

This work empirically verify that the weak formulation, with a proper choice of test function and integration domain, effectively filters noisy data and explains why a weak form loss function is analogous to fitting a model to filtered data and provides a practical way to parameterize the weak form.

Xuyang Li, J. Harlim, R. Maulik · 1 citation · ⚡1

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