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韦克·辛迪:基于Galerkin的数据驱动模型选择
Daniel A Messenger1, David M Bortz1
1Department of Applied Mathematics, University of Colorado, Boulder, CO 80309-0526 USA.
我们开发了微弱SINDy (WSINDy),一种用于从噪音数据中发现微分方程的新方法. WSINDy可靠地识别模型,即使有很大的噪音,提高准确性,并使可靠的预测.
科学领域:
- 科学计算是科学计算.
- 数据驱动的建模.
- 不同方程的微分方程.
背景情况:
- 从数据中发现治理方程对于科学建模至关重要.
- 像SINDy这样的现有方法与杂的数据集作斗争.
- 准确的方程发现需要对测量错误进行强有力的处理.
研究的目的:
- 引入一种新的弱公式和离散式,用于从噪音数据中学习微分方程.
- 开发一种强大而准确的方法,用于管理方程的稀疏恢复.
- 改进标准SINDy算法用于噪音数据场景.
主要方法:
- 开发了一个弱公式,用线性转换和差异减少取代点向导数近似.
- 介绍了软弱的SINDy (WSINDy) 算法.
- 利用整合来减少自然噪音,灵感来自Schaeffer和McCalla (2017).
主要成果:
- 通过WSINDy,可以从具有高噪声水平 (比率>0.1) 的数据中可靠地识别模型.
- 该算法减少了系数误差,导致了准确的预测.
- 误差系数与噪声线性扩展,在低噪声条件下确保高精度.
结论:
- 对于噪音数据,WSINDy提供了一个强大的,准确的替代标准SINDy.
- 该方法将SINDy的效率与降噪能力相结合.
- WSINDy 便于从经验测量中可靠地得到微分方程的稀疏恢复.
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