Iterative symbolic regression for learning transport equations
Iterative symbolic regression for learning transport equations
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DOI：
10.1002/aic.17695
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发表时间：
2022-03-31
期刊：
影响因子：
3.7
通讯作者：
White, Andrew D.
中科院分区：
文献类型：
--
作者：
Ansari, Mehrad;Gandhi, Heta A.;White, Andrew D.
计算流体动力学（CFD）分析在化学工程中有着广泛的应用。虽然CFD计算是准确的，但与复杂系统相关的计算成本使得难以获得系统变量之间的经验方程。在这里，我们结合联合收割机主动学习（AL）和符号回归（SR），从CFD模拟得到系统变量的符号方程。基于高斯过程回归的AL允许通过从可用的可能参数范围中选择最有指导意义的点来自动选择变量。然后将这些实验的结果传递给SR，以找到CFD模型的经验符号方程。这种方法是可扩展的，适用于任何所需数量的CFD设计参数。为了证明这种方法的有效性，我们用两个模型系统。我们恢复的经验公式的压力下降在弯曲的管道和一个新的方程预测回流在心脏瓣膜下主动脉瓣关闭不全。
Computational fluid dynamics (CFD) analysis is widely used in chemical engineering. Although CFD calculations are accurate, the computational cost associated with complex systems makes it difficult to obtain empirical equations between system variables. Here, we combine active learning (AL) and symbolic regression (SR) to get a symbolic equation for system variables from CFD simulations. Gaussian process regression-based AL allows for automated selection of variables by selecting the most instructive points from the available range of possible parameters. The results from these experiments are then passed to SR to find empirical symbolic equations for CFD models. This approach is scalable and applicable for any desired number of CFD design parameters. To demonstrate the effectiveness, we use this method with two model systems. We recover an empirical equation for the pressure drop in a bent pipe and a new equation for predicting backflow in a heart valve under aortic insufficiency.