Nonlinear regression operating on microstructures described from topological data analysis for the real-time prediction of effective properties

Yun, M. ; Argerich, C. ; Cueto, E. (Universidad de Zaragoza) ; Duval, J.L. ; Chinesta, F.

Resumen: Real-time decision making needs evaluating quantities of interest (QoI) in almost real time. When these QoI are related to models based on physics, the use ofModel Order Reduction techniques allows speeding-up calculations, enabling fast and accurate evaluations. To accommodate real-time constraints, a valuable route consists of computing parametric solutions-the so-called computational vademecums-that constructed off-line, can be inspected on-line. However, when dealing with shapes and topologies (complex or rich microstructures) their parametric description constitutes a major difficulty. In this paper, we propose using Topological Data Analysis for describing those rich topologies and morphologies in a concise way, and then using the associated topological descriptions for generating accurate supervised classification and nonlinear regression, enabling an almost real-time evaluation of QoI and the associated decision making.

Idioma: Inglés

DOI: 10.3390/ma13102335

Año: 2020

Publicado en: MATERIALS 13, 10 (2020), 2335
ISSN: 1996-1944

Factor impacto JCR: 3.623 (2020)

Categ. JCR: METALLURGY & METALLURGICAL ENGINEERING rank: 17 / 80 = 0.213 (2020) - Q1 - T1
Categ. JCR: MATERIALS SCIENCE, MULTIDISCIPLINARY rank: 152 / 333 = 0.456 (2020) - Q2 - T2
Categ. JCR: PHYSICS, CONDENSED MATTER rank: 27 / 69 = 0.391 (2020) - Q2 - T2
Categ. JCR: PHYSICS, APPLIED rank: 51 / 160 = 0.319 (2020) - Q2 - T1
Categ. JCR: CHEMISTRY, PHYSICAL rank: 79 / 162 = 0.488 (2020) - Q2 - T2

Factor impacto SCIMAGO: 0.682 - Materials Science (miscellaneous) (Q2) - Condensed Matter Physics (Q2)

Tipo y forma: Artículo (Versión definitiva)

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