Figure 2: Inference and design combination.
From: Accelerated search for materials with targeted properties by adaptive design
The relative performance of various regressor:selector combinations on the NiTi SMA training data set. On the abscissa, we plot the number of initial random picks, taken from the training set, for building the statistical inference model. On the ordinate, we plot the average number of picks required to find the alloy in the training set with the lowest thermal hysteresis (ΔT). The best regressor:selector finds the optimal alloy in as few picks as possible. We conclude that SVRrbf:KG (continuous red line) is the best regressor:selector combination for the NiTi SMA problem. Random picks are given as continuous blue line.
