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Parametric physics informed neural networks with nonlinear factor parameterization for non-Newtonian lubrication
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DOI：10.1016/j.triboint.2026.112513.png)
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Abstract
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• A PPINN is proposed for porous squeeze films with Rabinowitsch fluids. • Variable viscosity is captured in one physics-informed model. • PPINN matches perturbation and numerical solutions with high accuracy. • One network generalizes across β values without case-specific retraining.
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6.9
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