Selective boundary condition reduction via learned error gating
A framework for learning when one reduced boundary condition can replace another is introduced: paired solutions train a neural network to estimate the resulting domain and boundary errors, and the simpler condition is used only when both predicted errors meet prescribed tolerances.
Daniel Fernández, D. Penk, Dominik Riedelbauch
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