Evaluation of Parameter Sweeps for Computationally Efficient Infection Risk Analysis Using Pedestrian Dynamics
Evaluation of Parameter Sweeps for Computationally Efficient Infection Risk Analysis Using Pedestrian Dynamics
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DOI：
10.1109/aero55745.2023.10115639
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发表时间：
2023-03
期刊：
影响因子：
--
通讯作者：
T. Islam;A. Srinivasan;S. Namilae
中科院分区：
文献类型：
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作者：
T. Islam;A. Srinivasan;S. Namilae
行人动力学是一种用于对人的精细运动建模的方法。它越来越多地应用于分析航空旅行中直接传播疾病的感染风险。通常需要参数扫描来评估各种可能情况下的感染风险，以考虑人类行为的固有可变性。最近引入了低差异参数扫描，以减少一到三个数量级的计算工作量。然而，它有以下局限性：（i）低开销的并行化导致显着的负载不平衡，以及（ii）收敛速度随维数而变化。本文探讨伪随机和混合序列是否可以克服这些缺陷，以及是否可以改变收敛标准，以更快地产生准确的解决方案。我们使用不同的参数扫描策略模拟飞机的离机过程，并评估其相对的计算效率。我们的研究结果表明，混合和伪随机参数扫描是有利的中等精度，而低差异扫描是更可取的高精度。我们的研究结果还表明，收敛标准可以大大放宽，以产生准确的解决方案，约20倍的速度。他们承诺帮助各种应用程序，采用大参数扫描建模感染风险。
Pedestrian dynamics is an approach for modeling the fine-scaled movement of people. It is finding increasing application in the analysis of infection risk for directly transmitted diseases during air travel. A parameter sweep is often needed to evaluate infection risk for a variety of possible scenarios to account for inherent variability in human behavior. A low discrepancy parameter sweep was recently introduced to reduce the computational effort by one to three orders of magnitude. However, it has the following limitations: (i) a low-overhead parallelization leads to significant load imbalance, and (ii) the convergence rate worsens with dimension. This paper examines whether pseudorandom and hybrid sequences can overcome these defects and whether the convergence criteria can be changed to yield accurate solutions faster. We simulate the deplaning process of an airplane using different parameter sweep strategies and evaluate their relative computational efficiencies. Our results show that hybrid and pseudorandom parameter sweeps are advantageous for moderate accuracy, while a low discrepancy sweep is preferable for high accuracy. Our results also show that the convergence criteria could be relaxed substantially to yield accurate solutions around a factor of 20 faster. They promise to help a variety of applications that employ large parameter sweeps for modeling infection risk.