基于物理的基因编程,用于从稀缺和杂的数据中发现部分微分方程
Benjamin G Cohen1, Burcu Beykal1,2, George Bollas1
1Department of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, 06269, CT, USA.
概括
本研究引入了一个新的框架,使用符号回归和遗传编程从有限的,杂的数据中发现部分微分方程 (PDEs),优于复杂系统的现有方法.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 计算科学 计算科学
- 应用数学 应用数学 应用数学
背景情况:
- 发现复杂系统的治理方程对于科学进步至关重要.
- 传统的方法在稀缺和杂的实验数据下扎.
- 部分微分方程 (PDEs) 是许多物理现象的建模的基础.
研究的目的:
- 从稀缺和杂的数据中开发一种新的框架来识别自由形式的PDEs.
- 为了证明框架对合成系统的有效性.
- 将其性能与现有方法进行比较,例如弱非线性动力学 (SINDy) 的 Sparse 识别.
主要方法:
- 使用遗传编程进行象征回归.
- 从合成系统收集时间变量数据.
- 用弱SINDy进行比较分析.
主要成果:
- 成功识别了四个合成系统的地面真实PDE模型.
- 在数据稀缺的场景中,在较弱的SINDy上表现优越.
- 展示了对噪声和数据稀缺性的稳定性,从最小的数据点 (8个时间序列) 恢复模型,噪声高达50%.
结论:
- 拟议的框架有效地识别了有意义的PDE模型,即使数据有限和杂.
- 它为PDE发现提供了一个强大的解决方案,在数据采集具有挑战性或昂贵的情况下.
- 这种方法具有很大的潜力,可以在数据有限的领域推进科学发现.
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