An adaptive sampling algorithm that triangulates the response manifold and adds barycenter points guided by a residual threshold produces surrogate-training data that lowers neural network prediction error versus one-shot samplers in a 1D harmonic transport test case.
Adaptive sampling strategies for non-intrusive pod-based surrogates
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An adaptive sampling algorithm for data-generation to build a data-manifold for physical problem surrogate modeling
An adaptive sampling algorithm that triangulates the response manifold and adds barycenter points guided by a residual threshold produces surrogate-training data that lowers neural network prediction error versus one-shot samplers in a 1D harmonic transport test case.