A hybrid model integrates a first-principles model (based on physical laws) with a machine learning model (data-driven predictions) to enhance accuracy and flexibility.
The first-principles model provides a structured, mechanistic understanding of the system, while machine learning fills in the gaps, manages uncertainties, and adapts to complex, real-world scenarios. By combining these, the fundamental engineering laws in the first-principles model work together with data-driven algorithms in the prediction model to optimize compressor performance.
