Combining finite basis PINNs with hard boundary constraints keeps Stokes-flow errors near one percent as the number of perforations grows to 100.
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Finite basis physics-informed neural networks with hard constraints for viscous fluid flow in highly perforated domains
Combining finite basis PINNs with hard boundary constraints keeps Stokes-flow errors near one percent as the number of perforations grows to 100.