A PirateNet-based physics-informed neural network solves level set interface transport benchmarks to low L2 error without upwind stabilization, though the 'state-of-the-art' claim is tied to in-sample hyperparameter tuning.
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Physics-informed neural networks for solving moving interface flow problems using the level set approach
A PirateNet-based physics-informed neural network solves level set interface transport benchmarks to low L2 error without upwind stabilization, though the 'state-of-the-art' claim is tied to in-sample hyperparameter tuning.