Author

Sebastian Loeschcke

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#machine learning Preprint Sep 2026

Planetary Feature Fields are Scalable Earth Representations

Satellite observations, precomputed embeddings, and map products describe the same evolving Earth, yet are stored as independent, petabyte-scale data products. Their continued growth calls for compact representations of multiple products while preserving spatial and temporal detail. We introduce Planetary Feature Field...

Arjun Rao, Sebastian Loeschcke, A. Fuller et al. · 0 citations

#artificial intelligence Preprint Aug 2026

SS-ESOAP: Self-Scaled Adaptive Preconditioning for Physics-Informed Learning

Method is introduced, which augments SOAP-style preconditioning with a scalar secant-energy correction adapted to Kronecker geometry and an adaptive basis update followed by variance-state downscaling, and is positioned as a scalable option for stiff, high-accuracy physics-informed training, rather than a uniform repla...

Guang-Yuan Wang, Mads Toftrup, Sebastian Loeschcke et al. · 0 citations