Data-driven methods provide model creation tools for systems where the application of conventional analytical methods is restrained. The proposed method involves the data-driven derivation of a partial differential equation (PDE) for process dynamics, helping process simulation and study. The paper describes the methods that are used within the EPDE (Evolutionary Partial Differential Equations) partial differential equation discovery framework [1]. The framework involves a combination of evolutionary algorithms and sparse regression. Such an approach is versatile compared to other commonly used data-driven partial differential derivation methods by making fewer assumptions about the resulting equation. This paper highlights the algorithm features that allow data processing with noise, which is similar to the algorithm's real-world applications. This paper is an extended version of the ICCS-2020 conference paper [2].

数据驱动的方法为传统分析方法应用受到限制的系统提供模型创建工具。所提出的方法涉及过程动力学偏微分方程（PDE）的数据驱动推导，有助于过程模拟和研究。该论文描述了在 EPDE（进化偏微分方程）偏微分方程发现框架 [1] 中使用的方法。该框架涉及进化算法和稀疏回归的组合。与其他常用的数据驱动偏微分推导方法相比，这种方法是通用的，通过对结果方程进行较少的假设。本文重点介绍了允许带有噪声的数据处理的算法特性，这类似于该算法的实际应用。