Parallel three-dimensional simulations of quasi-static elastoplastic solids

Boffi, Nicholas M.; Rycroft, Chris H.

doi:10.1016/j.cpc.2020.107254

Cited by 6 publications

(2 citation statements)

References 97 publications

(129 reference statements)

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“…Other parameters are defined in Table 2. In this example, a numerical scheme developed by Rycroft et al [57,51] is used to solve the system of Eqs. (6.9-6.10) .…”

Section: Dielectric Cylinder In Homogeneous Electric Fieldmentioning

confidence: 99%

Grassmannian diffusion maps based surrogate modeling via geometric harmonics

Santos,

Giovanis,

Kontolati

et al. 2021

Preprint

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In this paper, a novel surrogate model based on the Grassmannian diffusion maps (GDMaps) and utilizing geometric harmonics is developed for predicting the response of engineering systems and complex physical phenomena. The method utilizes the GDMaps to obtain a low-dimensional representation of the underlying behavior of physical/mathematical systems with respect to uncertainties in the input parameters. Using this representation, geometric harmonics, an out-of-sample function extension technique, is employed to create a global map from the space of input parameters to a Grassmannian diffusion manifold. Geometric harmonics is also employed to locally map points on the diffusion manifold onto the tangent space of a Grassmann manifold. The exponential map is then used to project the points in the tangent space onto the Grassmann manifold, where reconstruction of the full solution is performed. The performance of the proposed surrogate modeling is verified with three examples. The first problem is a toy example used to illustrate the development of the technique. In the second example, errors associated with the various mappings employed in the technique are assessed by studying response predictions of the electric potential of a dielectric cylinder in a homogeneous electric field. The last example applies the method for uncertainty prediction in the strain field evolution in a model amorphous material using the shear transformation zone (STZ) theory of plasticity. In all examples, accurate predictions are obtained, showing that the present technique is a strong candidate for the application of uncertainty quantification in large-scale models.

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“…Other parameters are defined in Table 2. In this example, a numerical scheme developed by Rycroft et al [57,51] is used to solve the system of Eqs. (6.9-6.10) .…”

Section: Dielectric Cylinder In Homogeneous Electric Fieldmentioning

confidence: 99%

Grassmannian diffusion maps based surrogate modeling via geometric harmonics

Santos,

Giovanis,

Kontolati

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Other parameters are defined in Table 2. In this example, a numerical scheme developed by Rycroft et al 66,67 is used to solve the system of Equations ( 35) and (36). This method utilizes an Eulerian finite-difference method under quasi-static conditions.…”

mentioning

confidence: 99%

Grassmannian diffusion maps based surrogate modeling via geometric harmonics

Santos

Giovanis

Kontolati

et al. 2022

Numerical Meth Engineering

View full text Add to dashboard Cite

A novel surrogate model based on the Grassmannian diffusion maps (GDMaps) and utilizing geometric harmonics (GH) is developed for predicting the response of complex physical phenomena. The method utilizes GDMaps to obtain a low-dimensional representation of the underlying behavior of physical/ mathematical systems with respect to uncertain input parameters. Using this representation, GH, an out-of-sample extension technique, is employed to create a global map from the input parameter space to a Grassmannian diffusion manifold. GH is further employed to locally map points on the diffusion manifold onto the tangent space of a Grassmann manifold. The exponential map is then used to project the points in the tangent space onto the Grassmann manifold, where reconstruction of the full solution is performed. The performance of the proposed surrogate model is verified with three examples. The first problem is a toy example used to illustrate the technique. In the second example, errors associated with the various mappings are assessed by studying response predictions of the electric potential of a dielectric cylinder in a homogeneous electric field. The last example applies the method for uncertainty prediction in the strain field evolution in a model amorphous material using the shear transformation zone theory of plasticity.

show abstract