使用自适应设计和元建模方法对功能结构小麦模型进行高效贝叶斯自动校准
Emmanuelle Blanc1, Jérôme Enjalbert1, Timothée Flutre1
1Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, 91190, Gif-sur-Yvette, France.
Journal of experimental botany
|August 26, 2023
概括
校准像WALTer这样复杂的功能结构工厂模型是具有挑战性的,因为计算成本. 本研究引入了一种高效的贝叶斯方法,使用高斯过程元模型准确估计参数并量化小麦耕作动态中的不确定性.
科学领域:
- 植物建模 植物建模
- 计算生物学是一种计算生物学.
- 农业科学 农业科学
背景情况:
- 功能结构植物模型 (FSPM) 是植物科学研究的重要工具.
- 模型校准经常受到高计算需求的阻碍,导致错误传播被忽视.
研究的目的:
- 开发和应用WALTer功能结构小麦模型的自动校准方法.
- 解决校准复杂工厂模型和量化参数不确定性的计算挑战.
主要方法:
- 利用贝叶斯校准方法估计了五个关键参数及其不确定性.
- 采用高斯过程元模型来降低WALTer模型的计算成本.
- 实现了适应性设计,并使用高效的全球优化算法进行模型校准.
主要成果:
- 提出的方法成功地使用合成和实验数据校准了WALTer模型.
- 证明了高斯过程元模型在减轻计算负担方面的效率.
- 通过贝叶斯框架有效量化参数不确定性.
结论:
- 提出的自动校准方法对于WALTer小麦模型是有效的.
- 这种方法为校准其他复杂的功能结构植物模型提供了有价值的解决方案.
- 降低了计算成本,提高了工厂模型参数化的准确性.
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