Gray–Scott model is one of the most well-known reaction–diffusion models which has a wealth of spatiotemporal chaos behavior. It is commonly used to study spatiotemporal chaos. In the paper, a novel method is proposed for the identification of the Gray–Scott model via deterministic learning and interpolation. The method mainly consists of two phases: the local identification phase and the global identification phase. Local identification is achieved using the finite difference method and deterministic learning. Based on the local identification results, the interpolation method is employed to obtain global identification. Numerical experiments show the feasibility and effectiveness of the proposed method.

中文翻译：

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通过确定性学习识别 Gray-Scott 模型

Gray-Scott 模型是最著名的反应-扩散模型之一，具有丰富的时空混沌行为。它通常用于研究时空混沌。在本文中，提出了一种通过确定性学习和插值来识别 Gray-Scott 模型的新方法。该方法主要包括两个阶段：局部识别阶段和全局识别阶段。使用有限差分法和确定性学习实现局部识别。根据局部识别结果，采用插值法获得全局识别。数值实验表明了所提方法的可行性和有效性。