Benchmark study finds quantile and z-score marking strategies most robust for adaptive mesh refinement in steady mechanics problems, with Dörfler effective at large parameters and Isolation Forest competitive only under generous settings.
Recurrent neural networks as optimal mesh refinement strategies.Computers and Mathematics with Applications, 97:61–76, 2021
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Marking strategies for adaptive mesh refinement: An efficiency-focused benchmark study for steady solid and fluid mechanics problems
Benchmark study finds quantile and z-score marking strategies most robust for adaptive mesh refinement in steady mechanics problems, with Dörfler effective at large parameters and Isolation Forest competitive only under generous settings.