Active Learning with Selective Time-Step Acquisition for PDEs
Yegon Kim; Hyunsu Kim; Gyeonghoon Ko; Juho Lee · 2025 · arXiv
WASTE classifies this as Abandoned Hypothesis · AI classification, approximate
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Abstract (excerpt)
Accurately solving partial differential equations (PDEs) is critical to understanding complex scientific and engineering phenomena, yet traditional numerical solvers are computationally expensive. Surrogate models offer a more efficient alternative, but their development is hindered by the cost of generating sufficient training data from numerical solvers. In this paper, we present a novel framework for active learning in PDE surrogate modeling that reduces this cost. Unlike the existing AL methods for PDEs that always acquire entire PDE trajectories, our approach, STAP (**S**elective **T**ime
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Metadata source: arXiv