在规模的合野火-大气模拟中的不确定性量化
Paul Schwerdtner1, Frederick Law1, Qing Wang2
1Courant Institute of Mathematical Sciences, New York University, 251 Mercer Street, New York, NY 10012, USA.
PNAS nexus
|December 23, 2024
概括
利用受过相关数据训练的代用模型显著加速野火模拟,以更好地量化不确定性. 这种方法大大减少了计算时间,并提高了预测野火影响的准确性.
科学领域:
- 计算科学是一种计算科学.
- 环境建模环境建模
- 野火的动态 野火的动态
背景情况:
- 野火模拟对于消防管理和疏散规划至关重要.
- 在高保真性野火模型中量化不确定性是计算上昂贵的.
- 目前的方法与现代野火的规模和强度作斗争.
研究的目的:
- 在野火模拟中开发一种可扩展的多真实性方法来量化野火模拟中的不确定性.
- 为了证明在相关数据上训练的代孕模型的有效性.
- 为了降低野火不确定性量化计算成本.
主要方法:
- 利用在偏见但相关的数据上训练的代孕模型.
- 实施多忠实度方法,结合代用模型和高忠实度模型.
- 将该方法应用于数十亿自由度的大规模野火模拟.
主要成果:
- 将不确定性定量化的培训时间缩短了几个数量级 (从3个月减少到3小时以下).
- 在固定预算内,与高保真模拟相比,在烧毁区域预测中至少达到两倍的准确性.
- 证明了对大规模野火场景的多忠度不确定性量化的实用性.
结论:
- 在相关数据上训练的替代模型对于计算上昂贵的模拟是有效的.
- 相关性,而不是偏差,是加速多忠实性方法中不确定性量化的关键.
- 该方法为野火模拟不确定性提供了一个可扩展的解决方案,并在科学计算中具有更广泛的应用.
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