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An amortized neural sampler that combines operator learning with flow methods for sampling, enabling efficient sampling across families of stochastic differential equations, and theoretically establishes the expressivity and resolution invariance of the framework.
CHOP achieves superior performance on OOD tasks by utilizing a frozen ICON, revealing that interpretability and generalization can coexist in operator learning.
