2021
DOI: 10.1115/1.0002994v
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Transfer Optimization in Accelerating the Design of Turbomachinery Cascades

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“…Hence, as the iteration goes on, the accuracy of singlefidelity GP built with much more target samples can become better than that of multifidelity GP (Guo et al, 2018). And then, "negative transfer" (Wang et al, 2020) happens, i.e., the information gained from the source task misleads the algorithm to query samples in areas other than the vicinity of the real optimal solution. Hence, STO achieves worse optimal solutions than that of EGO at the end.…”
Section: Design Optimization Of a Lowspeed Airfoilmentioning
confidence: 99%
“…Hence, as the iteration goes on, the accuracy of singlefidelity GP built with much more target samples can become better than that of multifidelity GP (Guo et al, 2018). And then, "negative transfer" (Wang et al, 2020) happens, i.e., the information gained from the source task misleads the algorithm to query samples in areas other than the vicinity of the real optimal solution. Hence, STO achieves worse optimal solutions than that of EGO at the end.…”
Section: Design Optimization Of a Lowspeed Airfoilmentioning
confidence: 99%