A PINN recovers jet-impingement convective heat transfer coefficients from sparse, noisy in-solid temperatures, matching CHT-based benchmarks with relative errors below 8% for noise up to 10% and sampling rates at or above 0.5 s^{-1}.
General principles of controlled water cooling for metallurgical on-line hot rolling processes: forced flow and sprayed surfaces with film boiling regime and rewetting phenomena
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Physics-Informed Neural Networks for Estimating Convective Heat Transfer in Jet Impingement Cooling: A Comparison with Conjugate Heat Transfer Simulations
A PINN recovers jet-impingement convective heat transfer coefficients from sparse, noisy in-solid temperatures, matching CHT-based benchmarks with relative errors below 8% for noise up to 10% and sampling rates at or above 0.5 s^{-1}.