Operating fluids are always a significant factor for not achieving good enough performance of heat transfer equipment and also for growing the energy costs. To resolve this issue, nanofluids are considered as a potential choice for the conventional heat transfer fluids due to their efficiency to improve overall thermal performance. The aim of this research is to propose a physics guided machine learning approach by incorporating physics-based relations at the initial stage and into traditional loss functions for predicting the thermal conductivity of water-based nanofluids using a wide range of both experimental and simulated data of nanoparticles Al2O3, CuO, and TiO2. Further, smart connectionist methods, viz. Ridge regression, Lasso regression, Random Forest, Extreme Gradient Boosting (XGBoost), and black-box multi-layer perceptron (MLP) are applied to compare the present Physics-aware MLP model based on different statistical indicators. The accuracy analyses reveal that the use of physical views to monitor the learning of neural networks shows better results with MAPE = 0.7075%, RMSE = 0.0042 W/mK and R2 = 0.9525. The temperature and volume concentration variations are discussed graphically. Furthermore, the outcomes of applied algorithms confirm that the well-known theoretical and computer-aided models show substandard results than the proposed model.

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