Hybrid Analysis and modelling techniques aims to combine the interpretability, robust foundation and understanding of a physics-based approach with the accuracy, efficiency, and automatic pattern-identification capabilities of advanced data-driven machine learning and artificial intelligence algorithms. These hybrid approaches can also be categorised into intrusive hybrid modelling and non-intrusive hybrid modelling. The intrusive hybrid approach involves techniques like the Corrective Source Term Approach (CoSTA) that involves augmenting the governing equation of a physics-based model with a corrective source term generated using a deep neural network , while the non-intrusive hybrid methods involve techniques like non intrusive Reduced Order Models (ROMs) developed based on data compression and deep learning techniques. Here, we present some of our HAM work applied to a drilling process, a wind turbine and a greenhouse. Here, we use HAM to reconstruct temperature and velocity fields in the greenhouse and flow over a blade using non-intrusive ROMS, and in drilling process, we use HAM to improve the accuracy of a 1D cutting hole transport model to predict accurate pressures during the flow of cuttings using the COSTA and continual learning approach. Acknowledgements : For drilling, the data is provided by Aker Industry in KPN hole cleaning project funded by Research council and the work is done in collaboration with SINTEF Industry (Phillipe Nivlet, K Steinar, JO Skogestad, R Nybø,). Reference : Paper Number: SPE-214369-MS https://doi.org/10.2118/214369-MS .