Learning Where to Simulate: Generative Active Sampling for Online PDE Surrogate Training

arXiv:2606.09949v1 Announce Type: new Abstract: Data-driven PDE surrogates are trained with data produced by numerical PDE solvers. However, when the surrogate's goal is to generalize across a wide range of PDE configurations (e.g., initial conditions and physical coefficients), generating a representative training set is non-trivial. Uniform sampling of configuration parameters often under-represents trajectories exhibiting challenging dynamics, leading to high prediction errors and large error variance in the trained surrogate. Online training, where data generation and surrogate training ar

Source: arXiv cs.LG — read the full report at the original publisher.

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