Mechanical MNIST – Unsupervised Learning Dataset
Files
dataset_tutorials
heterogeneous_pattern_mesh
random_displacement_markers
Date
2023
DOI
Authors
Nguyen, Quan
Lejeune, Emma
Version
OA Version
Citation
Abstract
The Mechanical MNIST dataset collection contains Finite Element simulations of heterogeneous materials undergoing applied displacement. Here, we introduce a new benchmark dataset designed specifically for assessing unsupervised learning methods where the goal is to discover patterns from unlabeled data. To obtain this dataset, we generate displacement fields from Finite Element simulations and uniformly sample approximately 1500 displacement markers on each domain of interest. Since unsupervised learning aims to identify patterns in labeled data, we provide a dataset where the primary objective is to explore unlabeled data, while simultaneously providing “ground truth” information to ultimately evaluate the efficacy of different unsupervised learning approaches. It is important to note however, that in the intended applications of these methods, ground truth information will likely be absent, particularly in experimental studies of intricate heterogeneous soft tissue. Broadly speaking, this computationally generated dataset mimics the behavior of soft materials, while simultaneously providing ground truth information for method evaluation. In total, the dataset contains the following combinations of conditions: 6 different heterogeneous material patterns, 2 constitutive models, 4 controlled boundary conditions, and 1 random boundary condition. Here, we include the tutorials for our dataset with the name “dataset_tutorials.pdf”. This document contains the information to understand the contents of our dataset, as well as the instructions on how to use the data. The many options from our dataset should enable researchers to explore unsupervised learning methods on soft materials.
Description
The paper “Segmenting Mechanically Heterogeneous Domain via Unsupervised Learning” can be found at <link to be posted>. All code necessary to reproduce these finite element simulations and the results in the paper is available on GitHub (https://github.com/quan4444/cluster_project). For questions, please contact Emma Lejeune (elejeune@bu.edu).
License
This dataset is distributed under the terms of the Creative Commons Attribution-ShareAlike 4.0 License. The finite element simulations were conducted by Quan Nguyen using the open source software FEniCS (https://fenicsproject.org).