Discretization-independent operator learning for partial differential equations
A new and general encode-approximate-reconstruct operator learning model that leverages learned neural representations of bases for input and output function distributions that is discretization-independent, making it particularly effective for multiresolution learning.
Jacob Hauck, Yanzhi Zhang
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