Table 2 Comparison between the best design solutions identified with the competing algorithms.
From: Non-myopic multipoint multifidelity Bayesian framework for multidisciplinary design
Method | \(f^*({\textbf {x}}^*)\) | \({\textbf {x}}^* = [ F_V^*, F_N^*, s_{TPS}^* ]\) | \(m^*_{TPS}\) | \(T^*_{TPS}\) | \(m^*_{P}\) |
|---|---|---|---|---|---|
EGO | 0.8999 (10.01 %) | \({\textbf {x}}^* = [ 33.63 \; kN, 0.969 \; kN, 0.0396 \; m ]\) | \(476.6 \;kg\) | \(1320 \; K\) | \(74.61 \; kg\) |
MFEI | 0.8717 (12.83 %) | \({\textbf {x}}^* = [ 35.67 \; kN, 1.561 \; kN, 0.0341 \; m ]\) | \(410.35 \; kg\) | \(1326 \; K\) | \(80.06 \; kg\) |
MFMES | 0.8963 (10.37 %) | \({\textbf {x}}^* = [ 35.97 \; kN, 2.046 \; kN, 0.0373 \; m ]\) | \(447.96 \; kg\) | \(1329 \; K\) | \(81.52 \; kg\) |
MFPI | 0.8921 (10.79 %) | \({\textbf {x}}^* = [ 35.40 \; kN, 0.691 \; kN, 0.0377 \; m ]\) | \(453.4 \; kg\) | \(1322 \; K\) | \(77.97 \; kg\) |
NM3-BO | 0.8202 (17.98 %) | \({\textbf {x}}^* = [ 29.53 \; kN, 0.807 \; kN, 0.0304 \; m ]\) | \(365.17 \; kg\) | \(1310 \; K\) | \(65.45 \; kg\) |
