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Digitization of manufacturing processes has led to an increase in availability of process data which has enabled the use of data-driven models to predict the outcomes of these manufacturing process. Data-driven models are instantaneous to simulate and can provide real-time predictions but lack any governing physics within their framework. When process data deviates from original conditions, the predictions from these models may not agree with physical boundaries. In such cases, the use of first principle-based models to predict process outcomes have proven to be effective but computationally inefficient. Data-driven model unlike their first-principle counterparts do not have the ability to scale across geometries. Thus, there remains a need to develop computationally efficient models with physical understanding about the process. Training data provided governs the range of the prediction of the data-driven model, thus these models also need for these models to adapt to new data, outside of the original domain is provided.In the first case study, the addition of physics-based boundary conditions to a neural network to improve its predictability for granule density and granule size distribution (GSD) for high shear granulation process was demonstrated. The physics-constrained neural network (PCNN) trained slower than traditional neural networks but with constrained outputs, it was able to predict the granule size growth within the steady growth regime accurately. When input data which violated physics-based boundaries was provided, the outputs from PCNN identified these points more accurately compared to other non-physics constrained neural network with an error of