In this paper, modeling parameters are systematically classified according to high-fidelity modeling requirements, and a normalized modeling process of high-fidelity simulation is proposed. Meanwhile, several parameter identification techniques including global sensitivity analysis, regularization for high-fidelity modeling, hybrid computational inverse algorithms, and uncertainty invers analysis methods are developed for high-fidelity modeling. Four engineering applications are presented to demonstrate the feasibility and efficiency of these parameter identification techniques and high-fidelity modeling process.