A dual-scale physics-informed neural network, regularized by a mesoscale flow solver, predicts fibrous tow permeability more accurately than standalone PINNs in 2D benchmarks, while simpler upscaling methods stay close to fully resolved simulations at much lower cost.
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Hybrid machine learning based scale bridging framework for permeability prediction of fibrous structures
A dual-scale physics-informed neural network, regularized by a mesoscale flow solver, predicts fibrous tow permeability more accurately than standalone PINNs in 2D benchmarks, while simpler upscaling methods stay close to fully resolved simulations at much lower cost.