Surrogate modeling for efficient analysis and design of engineering systems
Surrogate models, trained using a data-driven approach, have been extensively used to approximate the input/output relationship for expensive high-fidelity models (e.g., large-scale physical experiments and high-resolution computationally expensive numerical simulations). The computational efficiency of surrogate models is greatly increased compared with the high-fidelity models. Once trained, the original high-fidelity models can be replaced by the surrogate models to facilitate efficient subsequent analysis and design of engineering systems. The quality of surrogate based analysis and design ...
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