REAL TIME, COMPACT, DYNAMIC, TRANSFER LEARNING MODELS

A method and apparatus of a device for generating a fusion model for a physical system is described. In an exemplary embodiment, the device receives low-fidelity input data and low-fidelity output data that represent a low-fidelity measurement of a physical system. In addition, the device trains a first model to predict the low-fidelity output data using the low-fidelity input data. Furthermore, the device receives high-fidelity input data and high-fidelity output data that represent a high-fidelity measurement of the physical system. The device additionally invokes the first model with the high-fidelity input data to generate predicted low-fidelity data. The device further trains a second model to predict the high-fidelity output data using the high-fidelity input data augmented with the predicted low-fidelity data. In addition, the device creates a fusion model for the physical system based on the first model and the second model, the first model and the second model to receive input to the fusion model, the second model to receive output from the first model, and output of the fusion model corresponding to output of the second model.