Uncertainty Tube Visualization of Particle Trajectories
This work addresses the need for reliable uncertainty visualization in scientific and engineering applications where trustworthiness is critical, representing an incremental improvement in visualization methods.
The paper tackles the challenge of quantifying and visualizing uncertainty in neural network predictions of particle trajectories by introducing the uncertainty tube, a computationally efficient visualization method that accurately captures nonsymmetric uncertainty using established uncertainty quantification techniques.
Predicting particle trajectories with neural networks (NNs) has substantially enhanced many scientific and engineering domains. However, effectively quantifying and visualizing the inherent uncertainty in predictions remains challenging. Without an understanding of the uncertainty, the reliability of NN models in applications where trustworthiness is paramount is significantly compromised. This paper introduces the uncertainty tube, a novel, computationally efficient visualization method designed to represent this uncertainty in NN-derived particle paths. Our key innovation is the design and implementation of a superelliptical tube that accurately captures and intuitively conveys nonsymmetric uncertainty. By integrating well-established uncertainty quantification techniques, such as Deep Ensembles, Monte Carlo Dropout (MC Dropout), and Stochastic Weight Averaging-Gaussian (SWAG), we demonstrate the practical utility of the uncertainty tube, showcasing its application on both synthetic and simulation datasets.