论文标题
物理信息符号回归的计算框架，并直接整合域知识
A computational framework for physics-informed symbolic regression with straightforward integration of domain knowledge
论文作者
论文摘要
在许多科学领域中发现一个有意义的象征表达来解释实验数据是一个基本挑战。我们提出了一个新颖的开源计算框架，称为科学家机器方程探测器（Scimed），该框架将科学学科的智慧与科学家在循环的方法中融合在一起，并将其与最先进的符号回归（SR）方法相结合。粘稠的结合了一种基于遗传算法的包装器选择方法与自动机器学习和两个级别的SR方法。我们测试了一个沉降球的五个配置，具有和没有空气动力学的非线性阻力，并且在测量中有过多的噪声。我们表明，疲惫不堪的人足够强大，可以从数据中发现正确的物理有意义的符号表达式，并演示域知识的整合如何增强其性能。我们的结果表明，在这些任务上的性能要比最先进的SR软件包更好，即使在不集成知识的情况下。此外，我们演示了与当前的大多数SR系统不同的疲倦能够提醒用户可能缺少功能的功能。
Discovering a meaningful symbolic expression that explains experimental data is a fundamental challenge in many scientific fields. We present a novel, open-source computational framework called Scientist-Machine Equation Detector (SciMED), which integrates scientific discipline wisdom in a scientist-in-the-loop approach, with state-of-the-art symbolic regression (SR) methods. SciMED combines a wrapper selection method, that is based on a genetic algorithm, with automatic machine learning and two levels of SR methods. We test SciMED on five configurations of a settling sphere, with and without aerodynamic non-linear drag force, and with excessive noise in the measurements. We show that SciMED is sufficiently robust to discover the correct physically meaningful symbolic expressions from the data, and demonstrate how the integration of domain knowledge enhances its performance. Our results indicate better performance on these tasks than the state-of-the-art SR software packages , even in cases where no knowledge is integrated. Moreover, we demonstrate how SciMED can alert the user about possible missing features, unlike the majority of current SR systems.
