稀有动力模型的衍生品自由域信息数据驱动的发现
Siddharth Prabhu1, Nick Kosir1, Mayuresh V Kothare1
1Department of Chemical and Biomolecular Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States.
概括
这项研究介绍了DF-SINDy,这是一种利用噪音反应数据创建精确的运动模型的新方法. 通过整合领域知识,这种方法可以提高复杂化学反应的模型可靠性.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 应用数学 应用数学 应用数学
- 数据科学数据科学数据科学
背景情况:
- 数据驱动的动力模型对于工艺设计至关重要,但对实验噪声敏感.
- 从数据中学习动态系统的现有方法与噪音反应动力学相斗争.
研究的目的:
- 开发一种可靠的方法,从噪音反应数据中推断可解释的动力模型.
- 通过纳入领域知识,提高数据驱动动态模型的准确性和可靠性.
主要方法:
- 引入了一种无导数稀疏识别技术 (DF-SINDy),该技术将整数与导数相近,而不是导数.
- 纳入领域信息,包括质量平衡和化学原理,到模型发现过程中.
- 通过使用具有不同噪声水平,采样频率和实验计数的合成数据验证了该方法.
主要成果:
- 与标准SINDy方法相比,DF-SINDy确定了具有较低误差的模型.
- 将域知识纳入其中显著改善了正确动力学项的恢复.
- 在解释发现的模型方面证明了更好的可靠性.
结论:
- DF-SINDy提供了一种更强大的方法,可以从噪音数据中学习动态模型.
- 整合领域知识可以提高数据驱动动力学模型的准确性和可解释性.
- 这项工作推动了对复杂反应网络的化学不可知,可解释的动力模型的开发.
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