用新型不确定度管可视化神经网络预测的粒子轨迹
Uncertainty Tube Visualization of Particle Trajectories
- 设计超椭圆管捕捉非对称不确定性
- 结合多种量化方法验证实用性
- 适合需高可信度的科学仿真场景
神经网络在预测粒子轨迹方面显著提升了多个科学与工程领域的能力。然而，有效量化并可视化预测中的固有不确定性仍具挑战性。缺乏对不确定性的理解会严重影响神经网络模型在可靠性要求极高的应用中的可信度。本文提出一种新型、计算高效的不确定性管可视化方法，用于表示神经网络生成的粒子路径不确定性。核心创新在于设计并实现一种超椭圆管，能够准确捕捉并直观呈现非对称不确定性。通过集成深度集成、蒙特卡洛丢弃和随机权重平均-高斯等成熟不确定性量化技术，我们验证了该方法在合成数据与模拟数据集上的实际效用。
原文摘要 · Abstract (English)
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.
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