dc.contributor.author Lejeune, Emma en_US dc.date.accessioned 2020-08-05T19:27:42Z dc.date.available 2020-08-05T19:27:42Z dc.date.issued 2020-07 dc.identifier.uri https://hdl.handle.net/2144/41357 dc.description The paper "Exploring the potential of transfer learning for metamodels of heterogeneous material deformation" is forthcoming. All code necessary to reproduce the metamodels demonstrated in the manuscript is available on GitHub (https://github.com/elejeune11/Mechanical-MNIST-Transfer-Learning). For questions, please contact Emma Lejeune (elejeune@bu.edu). en_US dc.description.abstract Each dataset in the Mechanical MNIST collection contains the results of 70,000 (60,000 training examples + 10,000 test examples) finite element simulation of a heterogeneous material subject to large deformation. Mechanical MNIST is generated by first converting the MNIST bitmap images (http://www.pymvpa.org/datadb/mnist.html) to 2D heterogeneous blocks of material. Consistent with the MNIST bitmap ($28 \times 28$ pixels), the material domain is a $28 \times 28$ unit square. The material is Neo-Hookean with a varying modulus dictated by the input bitmap. The simulation results included here are the change in strain energy at a fixed level of applied displacement. The cases considered are as follows: en_US *UE: uniaxial extension, full fidelity dataset (fully refined mesh, quadratic triangular elements, applied displacement is $1/2$ of a side length); *EE: equibiaxial extension, full fidelity dataset; *3D: uniaxial extension and out of plane twist, full fidelity three dimensional dataset (fully refined mesh, quadratic tetrahedral elements, applied displacement is $1/7$ of a side length, twist is $\pi/8$ radians, block thickness is $1/7$ of a side length); *UE-CM-28: uniaxial extension, $28 \times 28 \times 2$ linear triangular elements; *UE-CM-14: uniaxial extension, $14 \times 14 \times 2$ linear triangular elements; *UE-CM-7: uniaxial extension, $7 \times 7 \times 2$ linear triangular elements; *UE-CM-7-quad: uniaxial extension, $7 \times 7 \times 2$ quadratic triangular elements; *UE-CM-4: uniaxial extension, $4 \times 4 \times 2$ linear triangular elements; *UE-CM-4-quad: uniaxial extension, $4 \times 4 \times 2$ quadratic triangular elements; *UE-perturb: uniaxial extension, applied displacement is a perturbation (.001 units); *UE-CM-28-perturb: uniaxial extension, $28 \times 28 \times 2$ linear triangular elements, applied displacement is a perturbation (.001 units). All simulations are conducted with the FEniCS computing platform (https://fenicsproject.org). The code to reproduce these simulations is hosted on GitHub (https://github.com/elejeune11/Mechanical-MNIST-Transfer-Learning). dc.language.iso en_US dc.rights This dataset is distributed under the terms of the Creative Commons Attribution-ShareAlike 4.0 License. The original MNIST bitmaps are from Yann LeCun (Courant Institute, NYU) and Corinna Cortes (Google Labs, New York) on PyMVPA (http://www.pymvpa.org/datadb/mnist.html) licensed with https://creativecommons.org/licenses/by-sa/3.0. The finite element simulations were conducted by Emma Lejeune using the open source software FEniCS (https://fenicsproject.org). en_US dc.rights.uri http://creativecommons.org/licenses/by-sa/4.0/ dc.subject MNIST en_US dc.title Mechanical MNIST - Multi-Fidelity en_US dc.type Dataset en_US
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Except where otherwise noted, this item's license is described as This dataset is distributed under the terms of the Creative Commons Attribution-ShareAlike 4.0 License. The original MNIST bitmaps are from Yann LeCun (Courant Institute, NYU) and Corinna Cortes (Google Labs, New York) on PyMVPA (http://www.pymvpa.org/datadb/mnist.html) licensed with https://creativecommons.org/licenses/by-sa/3.0. The finite element simulations were conducted by Emma Lejeune using the open source software FEniCS (https://fenicsproject.org).