从数据中发现部分微分方程的工作流:应用到树木生物质动态
Emilie Peynaud1,2, Paulin Melatagia3, Serge Stinckwich4
1CIRAD, UMR AMAP, F-34398 Montpellier, France.
MethodsX
|September 29, 2025
概括
本研究介绍了CEDI,这是一个计算工作流程,用于从数据中发现部分微分方程 (PDE) 模型,增强对复杂生命科学动态的理解,例如植被生长.
科学领域:
- 生命科学 生命科学
- 计算生物学 计算生物学
- 生态生态学 生态生态学
背景情况:
- 生命科学中的复杂动力学往往缺乏足够的理论模型.
- 数据驱动的方法可以补充模型开发的现有知识差距.
研究的目的:
- 介绍CEDI,一种通用的计算工作流程,用于从数据中发现部分微分方程 (PDE) 模型.
- 为了说明工作流在建模植被生物质动态中的应用.
主要方法:
- CEDI的工作流包括四个步骤:数据收集,提取,区分和识别.
- 该工作流被设计为通用,适用于各种动态,并包括现有的数据驱动的PDE发现方法.
主要成果:
- 在三种不同的3D树架构类型的生物质动态上成功测试了CEDI工作流.
- 工作流证明了它从经验数据中发现PDE模型的能力.
结论:
- CEDI为数据驱动的PDE发现提供了一个全面的框架.
- 当有适当的数据和设置可用时,工作流可以作为建模各种动态的工具.
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