Fig. 1. Schematics of the proposed self-directed online learning optimization.

a Schematic illustration of self-directed online training. The initial batch of training data (light-blue dots) is randomly located. The DNN f₁ (dashed light-blue line) trained on the first batch of data only gives a rough representation of the true objective function F (solid black line). The second batch of training data (dark-blue dots) are generated by adding disturbance (orange curve) to the minimum of f₁. After trained with two batches, the DNN f₂ (dashed dark-blue line) is more refined around the minimum (the region of interest), while remains almost the same at other locations such as the right convex part. f₂ is very close to finding the exact global minimum point. b Flow diagram of the algorithm.