Koopman operators and Extended Dynamic Mode Decomposition for a pair of forward and reverse chemical reactions which occur simultaneously

J. Leventides, E. Melas, C. Poulios
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Abstract

We apply the Koopman operator theory and Extended Dynamic Mode Decomposition in a pair of forward and reverse chemical reactions which occur simultaneously with comparable speeds. The system of ODES which governs the evolution of the concentration of the reactants constitutes a nonlinear dynamical system with an interesting feature: It possesses uncountable infinite equilibria which reside on an algebraic surface. Koopman operator captures the dynamics of a nonlinear system, however it is infinite dimensional. In this study, we approximate the chemical reaction dynamics with a data-driven finite dimensional linear system which is defined on some augmented state space. We approximate so, with given initial conditions, the trajectories of the system and obtain an alternative description of the system based on Koopman operator theory, Extended Dynamic Mode Decomposition, and Machine Learning.
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同时发生的一对正反化学反应的Koopman算子和扩展动态模态分解
我们将Koopman算子理论和扩展动态模态分解应用于一对同时发生且速度相当的正反化学反应。控制反应物浓度演化的ODES系统构成了一个非线性动力系统,它具有一个有趣的特征:它具有存在于代数表面上的无数无限平衡。库普曼算子捕捉了非线性系统的动力学,但它是无限维的。在本研究中,我们用数据驱动的有限维线性系统来近似化学反应动力学,该系统被定义在一些增广的状态空间上。在给定初始条件下,我们近似了系统的轨迹,并基于Koopman算子理论、扩展动态模态分解和机器学习获得了系统的另一种描述。
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