Surrogate modeling of elasto-plastic problems via long short-term memory neural networks and proper orthogonal decomposition

Because of its nonlinearity and path-dependency, analysis of the elasto-plastic behavior of the finite element (FE) model is computationally expensive. By directly learning sequential data, modeling plasticity via deep learning has shown successful performance in immediately predicting the path-depe...
Ausführliche Beschreibung

Gespeichert in:
Autor*in:

Im, Sunyoung [verfasserIn]

Lee, Jonggeon

Cho, Maenghyo

Format:

E-Artikel

Sprache:

Englisch

Erschienen:

2021transfer abstract

Schlagwörter:

Elasto-plasticity

Surrogate model

Nonlinear model order reduction (MOR)

Proper orthogonal decomposition (POD)

Long short-term memory (LSTM)

Übergeordnetes Werk:

Enthalten in: Does enhanced hydration have impact on autogenous deformation of internally cued mortar? - Zou, Dinghua ELSEVIER, 2019, Amsterdam [u.a.]

Übergeordnetes Werk:

volume:385 ; year:2021 ; day:1 ; month:11 ; pages:0

Links:

Volltext

DOI / URN:

10.1016/j.cma.2021.114030

Katalog-ID:

ELV055093620

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