Articlediamond2024

The automated discovery of kinetic rate models – methodological frameworks

Miguel Ángel de Carvalho Servia · Ilya Orson Sandoval · King Kuok Hii · Klaus Hellgardt · Dongda Zhang · Ehecatl Antonio del Rio‐Chanona

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Optimal Experimental Design Methods Control Systems and Identification Machine Learning in Materials Science

Abstract

Two automated knowledge discovery methodologies (ADoK-S & ADoK-W) are created whereby symbolic regression, parameter estimation, information criteria and model-based design of experiments synergize for the optimized discovery of kinetic rate models.

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2006The scaling laws of human travel 2010Modelling the scaling properties of human mobility 2018Mechanistic models versus machine learning, a fight worth fighting for the biological community?1965Designs for Discriminating Between Two Rival Models 2009A Comprehensive Guide to Factorial Two-Level Experimentation 2019Model-based design of transient flow experiments for the identification of kinetic parameters 2022Distributional reinforcement learning for inventory management in multi-echelon supply chains

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