Invited Talk
UFO: Generalized Operator Learning for Earth System Modeling
Hanli Qiao · Invited Speaker
Abstract
Reactive transport and Earth system processes involve strongly coupled nonlinear dynamics across heterogeneous spatial, temporal, and spectral scales, posing significant challenges for conventional neural operators that rely on a single representation domain. In this talk, I present UFO, a generalized operator learning framework that enables adaptive interactions among physical, spectral, and latent representations without enforcing domain unification. UFO supports discretization-decoupled learning, allowing input functions to be observed at resolutions or locations different from training while enabling flexible solution querying across output discretizations. I will discuss the application of UFO to Earth system modeling problems, including reactive transport processes, and show how cross-domain operator realization can improve robustness under irregular sampling, nonlinear dynamics, and multi-scale structure.
Speaker Bio
Hanli Qiao is an invited speaker at AIEPS 2026.
Materials and Images
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