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

Learning Where to Simulate: Generative Active Sampling for Online PDE Surrogate Training arXiv:2606.09949v1 Announce Type: cross 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 tra