Data-augmented few-shot neural stencil emulation for computer-model system identification
This work proposes a more sample-efficient data-augmentation strategy for generating NPDE training data from a computer model—specifically, one using an explicit, structured-grid finite-difference numerical solver—by space-filling sampling of local ‘stencil’ states.