Deterministic and Stochastic Parameter Estimation for Polymer Reaction Kinetics I: Theory and Simple Examples,Macromolecular Theory and Simulations

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Deterministic and Stochastic Parameter Estimation for Polymer Reaction Kinetics I: Theory and Simple Examples
Macromolecular Theory and Simulations ( IF 1.8 ) Pub Date : 2021-06-13 , DOI: 10.1002/mats.202100017
Niklas Wulkow ₁ , Regina Telgmann ₂ , Klaus‐Dieter Hungenberg ₃ , Christof Schütte ₄ , Michael Wulkow ₂

Affiliation

Two different approaches to parameter estimation (PE) in the context of polymerization are introduced, refined, combined, and applied. The first is classical PE where one is interested in finding parameters which minimize the distance between the output of a chemical model and experimental data. The second is Bayesian PE allowing for quantifying parameter uncertainty caused by experimental measurement error and model imperfection. Based on detailed descriptions of motivation, theoretical background, and methodological aspects for both approaches, their relation are outlined. The main aim of this article is to show how the two approaches complement each other and can be used together to generate strong information gain regarding the model and its parameters. Both approaches and their interplay in application to polymerization reaction systems are illustrated. This is the first part in a two-article series on parameter estimation for polymer reaction kinetics with a focus on theory and methodology while in the second part a more complex example will be considered.

中文翻译：

聚合物反应动力学的确定性和随机参数估计 I：理论和简单示例

介绍、改进、组合和应用聚合背景下的两种不同的参数估计 (PE) 方法。第一个是经典 PE，其中人们对找到最小化化学模型输出与实验数据之间距离的参数感兴趣。第二个是贝叶斯 PE，允许量化由实验测量误差和模型缺陷引起的参数不确定性。基于对两种方法的动机、理论背景和方法学方面的详细描述，概述了它们的关系。本文的主要目的是展示这两种方法如何相互补充，并可以一起使用以产生关于模型及其参数的强大信息增益。说明了这两种方法及其在聚合反应系统应用中的相互作用。这是关于聚合物反应动力学参数估计的两篇文章系列的第一部分，重点是理论和方法论，而在第二部分中，将考虑一个更复杂的例子。

更新日期：2021-06-13

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