Presentation
Optimizing Uncertainty Estimation on Scientific Visualizations Using Learning Models
DescriptionScientific visualizations (SciVis) convert numerical and spatial data into images, enabling deeper insights into complex phenomena. Recent advancements in machine learning, particularly Deep Learning (DL), have significantly enhanced SciVis. By combining classical numerical techniques with DL, we can achieve the accuracies needed for real-world applications. However, DL models often exhibit undue confidence, especially with out-of-distribution (OOD) inputs, leading to misclassification with high confidence scores. To address this, we enhance DL models by quantifying uncertainty and enabling selective classification, allowing models to abstain from predictions when uncertainty is high. This approach outputs a prediction distribution, guiding users on when to seek human intervention. We evaluate this method across three tasks: airfoil pressure and velocity prediction using a Reynolds-Averaged Navier-Stokes (RANS) model, image classification with the ImageNet1K dataset, and digit recognition using the MNIST dataset.
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