• Aucun résultat trouvé

Learning corrections for hyperelastic models from data

N/A
N/A
Protected

Academic year: 2021

Partager "Learning corrections for hyperelastic models from data"

Copied!
13
0
0

Texte intégral

Figure

FIGURE 1 | Double thermal pendulum.
FIGURE 2 | Results for the thermal pendulum problem. Results are shown (see the detail in the small window in the bottom figure) for the ground truth (pseudo-experimental data), the uncorrected (purely Hamiltonian) assumed model and the corrected one.
FIGURE 3 | Loading process for one particular experiment. Pseudo-experimental response and prediction made by the standard (non-viscous) Mooney-Rivlin material.
FIGURE 4 | Comparison of the Mooney-Rivlin model prediction and its subsequent GENERIC correction with the experimental results for one particular experiment.
+3

Références

Documents relatifs

The asymptotic approximation of the displacement field deduced by orthogonal transformation, given by equations (81)-(84), can satisfy the symmetry property by an appropriate choice

This original coupled approach, using simulations from a realistic OGCM and in situ Nd data, allowed us to characterize the subduction areas, pathways and transports of the

Trois grandes parties ont été nécessaires : une première partie bibliographique à travers laquelle nous avons passé en revue les travaux actuels entrepris dans ce domaine, une

This text is based on notes (taken by R. Thangadurai) of three courses given at Goa University on December 15 and 16, 2014, on Algebraic independence of Periods of Elliptic

Motivated by the increase of archaeological and epigraphic sources in recent decades the project aims at an integrated analysis and transparent access of these

The fraction of reported cases, from date τ 3 to date τ f , the fraction of reported cases f was assumed increase linearly to reach the value 200% of its initial one and the fraction

data generating process is necessary for the processing of missing values, which requires identifying the type of missingness [Rubin, 1976]: Missing Completely At Random (MCAR)

Comparison of recognition rate when using only original data to the use of synthetic data, without (a) and with (b) highlighted times of addition of new class (dots). In Figure