A new methodology for anisotropic yield surface description using model order reduction techniques and invariant neural network

Producción científica: Article › revisión exhaustiva

Idioma original	English
Número de artículo	105542
Publicación	Journal of the Mechanics and Physics of Solids
Volumen	184
DOI	https://doi.org/10.1016/j.jmps.2024.105542
Estado	Published - mar 2024
Publicado de forma externa	Sí

ASJC Scopus Subject Areas

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title = "A new methodology for anisotropic yield surface description using model order reduction techniques and invariant neural network",

keywords = "Generic material model, Machine learning, Model reduction, Yield surface",

author = "Chady Ghnatios and Oana Cazacu and Benoit Revil-Baudard and Francisco Chinesta",

note = "Publisher Copyright: {\textcopyright} 2024 Elsevier Ltd",

year = "2024",

month = mar,

doi = "10.1016/j.jmps.2024.105542",

language = "English",

volume = "184",

}

TY - JOUR

T1 - A new methodology for anisotropic yield surface description using model order reduction techniques and invariant neural network

AU - Ghnatios, Chady

AU - Cazacu, Oana

AU - Revil-Baudard, Benoit

AU - Chinesta, Francisco

PY - 2024/3

Y1 - 2024/3

KW - Generic material model

KW - Machine learning

KW - Model reduction

KW - Yield surface

UR - https://www.scopus.com/pages/publications/85183472835

UR - https://www.scopus.com/pages/publications/85183472835#tab=citedBy

U2 - 10.1016/j.jmps.2024.105542

DO - 10.1016/j.jmps.2024.105542

M3 - Article

AN - SCOPUS:85183472835

SN - 0022-5096

VL - 184

JO - Journal of the Mechanics and Physics of Solids

JF - Journal of the Mechanics and Physics of Solids

M1 - 105542

ER -