Fig. 4: Performance of the model varying the number of sensors and their locations at inference time.
From: Development of the Senseiver for efficient field reconstruction from sparse observations
The x axis depicts number of sensors used to reconstruct the field. We tested our trained model with ten different random sensor locations (using fixed seeds) for each x coordinate. The plot shows the 10th and 90th percentiles as bounds of the error (equation (5)) and the average of the 10 with a line. Insets: predictions for the same time frame are shown to depict how the prediction accuracy increases qualitatively with more sensor coverage. All the colorbars are normalized from −1 to 1. δ, half-width of the channel.
