Apart from profiting on ML to accelerate the interpolation predictions of PBMs, there exists another way to integrate physics equations into data driven techniques. Physics-informed Machine Learning (PIML) is a modeling methodology which combines both PBMs and ML algorithms, to improve generalization and extrapolation capabilities, even when data is limited . These type of hybrid models are built by providing the data driven algorithms with different types of biases during the learning process in order to identify solutions that are physically consistent .