# Physics-Informed AI

Omani crude oil flows non-isothermally as a viscoplastic fluid, and predicting its heat and head losses has meant running high-fidelity CFD case by case, too slowly for real-time decisions. A physics-informed surrogate trained on 962 COMSOL simulations, with a dimensionless Rheological Heat-Loss Index (RHLI) as an input feature, now predicts total heat loss, head loss and total power loss together. Under five-fold cross-validation heat loss was the most learnable target, with cross-validated R² near 1; uncertainty quantification gave a mean calibration error of 0.0211 and 97.4% empirical 95% coverage, and SHAP ranked ambient temperature as the dominant heat-loss driver.

Omani crude oil flows non-isothermally as a viscoplastic fluid, and predicting its heat and head losses has meant running high-fidelity CFD case by case, too slowly for real-time decisions. A physics-informed surrogate trained on 962 COMSOL simulations, with a dimensionless Rheological Heat-Loss Index (RHLI) as an input feature, now predicts total heat loss, head loss and total power loss together. Under five-fold cross-validation heat loss was the most learnable target, with cross-validated R² near 1; uncertainty quantification gave a mean calibration error of 0.0211 and 97.4% empirical 95% coverage, and SHAP ranked ambient temperature as the dominant heat-loss driver.