PEDS datasets and figure data

<p>Datasets:</p><ul><li>y_fisher25.csv: reaction-diffusion equation; the thermal flux corresponding to structures with 25 holes</li><li>X_fisher25.csv: reaction-diffusion equation; the side lengths of the 25 holes in the structures</li><li>y_fisher16.csv: reaction-diffusion equation; the thermal flux corresponding to structures with 16 holes</li><li>X_fisher16.csv: reaction-diffusion equation; the side lengths of the 16 holes in the structures</li><li>y_fourier25.csv: diffusion equation; the thermal flux corresponding to structures with 25 holes</li><li>X_fourier25.csv: diffusion equation; the side lengths of the 25 holes in the structures</li><li>y_fourier16.csv: diffusion equation; the thermal flux corresponding to structures with 16 holes</li><li>X_fourier16.csv: diffusion equation; the side lengths of the 16 holes in the structures</li><li>y_maxwell10.csv: Helmholtz equation; the complex transmission through the 10-layered structure</li><li>X_maxwell10.csv: Helmholtz equation; the side lengths of the 10 holes in each layer of the structure followed by a one-hot encoding of the frequency [0.5, 0.75, 1]</li></ul><p>Figure data:</p><ul><li>nb_trainingpoints_Fig1.csv: number of training points in the dataset–x-coordinates (Fig 1, S1, and S2)</li><li>baseline_alFig1.csv: error of the baseline ensemble using a dataset that was generated using active learning (Fig 1, S1, and S2)</li><li>baseline_noalFig1.csv: error of the baseline ensemble using a dataset that was sampled uniformly at random (Fig 1, S1, and S2)</li><li>baseline_single_noalFig1.csv: error of the baseline (single model) using a dataset that was sampled uniformly at random (Fig 1, S1, and S2)</li><li>PEDS_alFig1.csv: error of the PEDS ensemble using a dataset that was generated using active learning (Fig 1, S1, and S2)</li><li>PEDS_noalFig1.csv: error of the PEDS ensemble using a dataset that was sampled uniformly at random (Fig 1, S1, and S2)</li><li>PEDS_single_noalFig1.csv: error of the PEDS (single model) using a dataset that was sampled uniformly at random (Fig 1, S1, and S2)</li><li>SM10_ALFigS1.csv: error of the space mapping ensemble with a resolution of 10 using a dataset that was generated using active learning (Fig S1)</li><li>SM10_noALFigS1.csv: error of the space mapping ensemble with a resolution of 10 using a dataset that was sampled uniformly at random (Fig S1)</li><li>SM10_single_noALFigS1.csv: error of the space mapping (single model) with a resolution of 10 using a dataset that was sampled uniformly at random (Fig S1)</li><li>SM20_single_noalFigS2.csv: error of the space mapping (single model) with a resolution of 20 using a dataset that was sampled uniformly at random (Fig S2)</li><li>SM20_ALFigS2.csv: error of the space mapping ensemble with a resolution of 20 using a dataset that was generated using active learning (Fig S2)</li><li>SM20_noALFigS2.csv: error of the space mapping ensemble with a resolution of 20 using a dataset that was sampled uniformly at random (Fig S2)</li><li>resolutionFigS4.csv: resolution of the middle fidelity model–x-coordinate (Fig. S4)</li><li>error_midfidFigS4.csv: error of the middle fidelity model (Fig. S4)</li></ul>