Learning stable reduced-order models for hybrid twins

Sancarlos, Abel ; Cameron, Morgan ; Le Peuvedic, Jean-Marc ; Groulier, Juliette ; Duval, Jean-Louis ; Cueto, Elias (Universidad de Zaragoza) ; Chinesta, Francisco

Resumen: The concept of “hybrid twin” (HT) has recently received a growing interest thanks to the availability of powerful machine learning techniques. This twin concept combines physics-based models within a model order reduction framework—to obtain real-time feedback rates—and data science. Thus, the main idea of the HT is to develop on-the-fly data-driven models to correct possible deviations between measurements and physics-based model predictions. This paper is focused on the computation of stable, fast, and accurate corrections in the HT framework. Furthermore, regarding the delicate and important problem of stability, a new approach is proposed, introducing several subvariants and guaranteeing a low computational cost as well as the achievement of a stable time-integration.

Idioma: Inglés

DOI: 10.1017/dce.2021.16

Año: 2021

Publicado en: Data-Centric Engineering 2 (2021), e10 [20 pp.]
ISSN: 2632-6736

Factor impacto CITESCORE: 1.6 - Engineering (Q3) - Mathematics (Q3) - Computer Science (Q3)

Financiación: info:eu-repo/grantAgreement/ES/MINECO-CICYT/DPI2017-85139-C2-1-R
Financiación: info:eu-repo/grantAgreement/ES/UZ/ESI-ENSAM-Simulated Reality

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Mec.Med.Cont. y Teor.Est. (Dpto. Ingeniería Mecánica)