Learning reduced-order Quadratic-Linear models in Process Engineering using Operator Inference

Autor:	Gosea, Ion Victor, Peterson, Luisa, Goyal, Pawan, Bremer, Jens, Sundmacher, Kai, Benner, Peter
Rok vydání:	2024
Předmět:	Mathematics - Numerical Analysis Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	In this work, we address the challenge of efficiently modeling dynamical systems in process engineering. We use reduced-order model learning, specifically operator inference. This is a non-intrusive, data-driven method for learning dynamical systems from time-domain data. The application in our study is carbon dioxide methanation, an important reaction within the Power-to-X framework, to demonstrate its potential. The numerical results show the ability of the reduced-order models constructed with operator inference to provide a reduced yet accurate surrogate solution. This represents an important milestone towards the implementation of fast and reliable digital twin architectures. Comment: 10 pages, 3 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2402.17698 Zobrazit plný text záznamu View this record from Arxiv