In these experiments, we used near field patches and used the loss function form the previous blog.

** Summary**:

sigma of f | \(\lambda \) | learning rate | number of epochs | batch size | Q1 | Q2 | Q1 per bubble | Q2 per bubble | % gap between training and validation | |

exp1 | 1 | 0.005 | 2e-5 | 300 | 1 | 13.450897 | 0.551683 | 0.527048 | 0.021537 | 30.9 |

epx2 | 2 | 0.01 | 2e-5 | 300 | 1 | 21.001895 | 0.823452 | 1.218658 | 0.047734 | 8.8 |

** Experiment 1: **learning rate=2e-5, sigma of kernel f=1, regularizer parameter=0.005

**Percentage gap between training and validation error** ( \( \frac{validation errror – training error}{training error}*100 \) ) = 30.9

**Average quantitative metric1** is 13.450897

**Average metric1 per bubble** is 0.527048

**Average quantitative metric2** is 0.551683

**Average metric2 per bubble** is 0.021537

Worst Case:Center Location(x,z) = Average Metric1 per bubble: 0.902738, Average Metric2 per bubble: : 0.056981

Best Case:Center Location(x,z) = Average Metric1 per bubble: 0.470359, Average Metric2 per bubble: : 0.014894

** Experiment 2: **learning rate=2e-5, sigma of kernel f=2, regularizer parameter=0.01

**Percentage gap between training and validation error** ( \( \frac{validation errror – training error}{training error}*100 \) ) = 8.8

**Average quantitative metric1** is 21.001895

**Average metric1 per bubble** is 0.823452

**Average quantitative metric2** is 1.218658

**Average metric2 per bubble** is 0.047734

Worst Case:Center Location(x,z) = Average Metric1 per bubble: 0.919580, Average Metric2 per bubble: 0.060028

Best Case:Center Location(x,z) = Average Metric1 per bubble:0.789220 , Average Metric2 per bubble: 0.044518