Surrogate-reformulation-assisted multitasking knowledge transfer for production optimization

Data-driven surrogate models, which are trained by samples to replace time-consuming numerical simulations, have been widely used to solve production optimization problems in recent years. It is a challenging and meaningful subject to research advanced surrogate-model-based methods that can obtain superior optimization performance within a limited time budget. The key is to enhance the quality of each training sample, i.e., the contribution of...