Goals:
- Provide benchmark datasets for machine learning methods applied to mechanics problems.
- Provide "mechanics relevant" examples for students getting started with machine learning.
- Make data-driven problems in mechanics more accessible to the broad research community.
Datasets:
- Mechanical MNIST
Data: https://open.bu.edu/handle/2144/39371
Code: https://github.com/elejeune11/Mechanical-MNIST
Manuscript: https://doi.org/10.1016/j.eml.2020.100659
Recommended Challenge: predict scalar and full field simulation outputs from input bitmaps describing heterogeneous material properties.
- Buckling Instability Classification (BIC)
Data: https://open.bu.edu/handle/2144/40085
Code: https://github.com/elejeune11/BIC
Manuscript: https://doi.org/10.1016/j.cad.2020.102948
Recommended Challenge: predict if a column is "stable" or "unstable" from input vectors describing heterogeneous material properties.
- Right Ventricular Myocardial Mechanics (RV Mechanics)
Data: https://dataverse.tdl.org/dataverse/RVMechanics
Code: n/a (all experimental data)
Manuscript: https://doi.org/10.1016/j.actbio.2020.12.006
Recommended Challenge: define a predictive data-driven constitutive equation for fibrous soft tissue.
- Blood Clot Simple Shear Testing Data
Data: https://dataverse.tdl.org/dataverse/BloodClotSimpleShear
Code: n/a (all experimental data)
Manuscript: https://www.sciencedirect.com/science/article/pii/S1751616120307566
Recommended Challenge: define a predictive data-driven constitutive equation for isotropic soft tissue.
- 3D Printed Crossed Barrel Dataset
Data: https://www.kablab.org/data
Code: n/a (experimental data and ABAQUS simulations)
Manuscript: https://advances.sciencemag.org/content/6/15/eaaz1708
Recommended Challenge: Find the optimal crossed barrel design with the lowest number of experiments.
- Young and Aged Mouse Skin Biaxial Mechanical Tests
Data: https://dataverse.tdl.org/dataverse/mouseskinmechanics1
Code: n/a (all experimental data)
Manuscript: https://doi.org/10.1016/j.actbio.2019.10.020
Recommended Challenge: define a predictive data-driven constitutive equation for planar soft tissue.
- Asymmetric Buckling Columns (ABC) dataset
Data: https://open.bu.edu/handle/2144/43730
Code: https://github.com/pprachas/ABC_dataset
Manuscript: https://arxiv.org/abs/2202.01380
Recommended Challenge: predict the buckling direction (left vs. right) from a spatially heterogenous column geometry.
- Crack Propagation in Steel Structures Under Ultra-Low Cycle Fatigue
Data: https://www.designsafe-ci.org/data/browser/public/designsafe.storage.published/PRJ-3394
Code: https://www.designsafe-ci.org/data/browser/public/designsafe.storage.published/PRJ-3394
Manuscript: https://stacks.stanford.edu/file/druid:ft563mt0968/Dissertation-augmented.pdf
Recommended Challenge: predict output QoIs for the "blind prediction" experimental dataset.
- Ductile Fracture in Structural Steel Inclined Notch Specimens
Data: https://purl.stanford.edu/qy227tf3022
Code: https://purl.stanford.edu/qy227tf3022
Manuscript: https://stacks.stanford.edu/file/druid:qy227tf3022/TR187_Smith.pdf
Recommended Challenge: predict force vs. displacement curves and fracture probability for held out experimental samples.
- Cardiac Motion Analysis Challenge
Data: http://www.cardiacatlas.org/challenges/motion-tracking-challenge/
Code: n/a
Manuscript: https://doi.org/10.1016/j.media.2013.03.008
Recommended Challenge: predict cardiac motion and strain from magnetic resonance (MR) and 3D ultrasound (3DUS) imaging datasets from a dynamic phantom and 15 healthy volunteers.
- Unsupervised Discovery of Interpretable Hyperelastic Constitutive Laws
Data: https://doi.org/10.3929/ethz-b-000505693
Code: https://euclid-code.github.io/
Manuscript: https://doi.org/10.1016/j.cma.2021.113852
Recommended Challenge: discover strain energy density functions from emulated DIC data created via the finite element method.
- Discovering Plasticity Models without Stress Data
Data: https://doi.org/10.3929/ethz-b-000534002
Code: https://euclid-code.github.io/
Manuscript: https://doi.org/10.1038/s41524-022-00752-4
Recommended Challenge: discover plastic yield surfaces and hardening laws from emulated DIC data created via the finite element method.
- Discrete Element Traction-Separation Data
Data: https://data.mendeley.com/datasets/n5v7hyny8n/1
Code: https://www.poromechanics.org/software--data.html
Manuscript: https://doi.org/10.1016/j.cma.2018.11.026
Recommended Challenge: derive theory-consistent and microstructure-based traction–separation laws.
- Experimental Quasi-Static and Impact Testing of Lattices
Data: https://www.kablab.org/lattice-quasi-static and https://www.kablab.org/lattice-impact
Code: n/a
Manuscript: https://doi.org/10.1016/j.matt.2022.06.051
Recommended Challenge: leverage quasi-static tests to make predictions relevant to impact test outcomes.
- Zero Modes and Classification of Combinatorial Metamaterials
Data: https://doi.org/10.5281/zenodo.7070963 and https://doi.org/10.5281/zenodo.7071282
Code: https://uva-hva.gitlab.host/published-projects/CNN_MetaCombi and https://uva-hva.gitlab.host/published-projects/CombiMetaMaterial
Manuscript: https://doi.org/10.1103/PhysRevLett.129.198003
Recommended Challenge: classify each kxk unit cell design into one of two classes (frequent incompatible class I) or (rare compatible class C).
- Comparisons for Neural Operators
Data: https://github.com/lu-group/deeponet-fno
Code: https://github.com/lu-group/deeponet-fno
Manuscript: https://doi.org/10.1016/j.cma.2022.114778
Recommended Challenge: benchmark neural operators across 16 examples.
- NeuroImaging Tools & Resources Collaboratory: Brain Biomechanics Imaging Resources
Data: https://www.nitrc.org/projects/bbir/
Code: https://www.nitrc.org/projects/bbir/
Manuscript: https://doi.org/10.1007/s10439-021-02820-0
Recommended Challenge: predict subject specific mechanical response of the human brain to in vivo loading.
Information for contributors:
- Consulting FAIR Principles (guidelines to improve the Findability, Accessibility, Interoperability, and Reuse of digital assets) is often helpful for effective data curation.
- Please make sure that the provided links will be long lasting. DOIs or handles can often be obtained through an institutional repository.
- Please be thoughtful about selecting licenses for data and code. For the Mechanical MNIST dataset, we chose a Creative Commons Attribution-ShareAlike 4.0 License for the data, and a MIT License for the code (we do not endorse any particular license choice).
- Keep file sizes small whenever possible. For example, it may make sense to create a HDF5 file.
- For the “Recommended Challenge,” data should be curated so that a user with limited familiarity can download all or part of the dataset and start working on the problem in just a few minutes. Think of the challenge as a starting point for what could be done with the data and code provided. Multiple datasets can have the same recommended challenge.
- If you have an interesting problem where regenerating the dataset from the provided code is quick and easy (i.e., can be done on a laptop in a few minutes by a user with no special expertise and with no need to install software other than Python and standard Python packages), we can link to just the code.
- It is not necessary to provide a link to a published manuscript. However, citing relevant manuscripts is one way to acknowledge the use of open datasets and code.
Links to other relevant pages:
- DesignSafe Data Depot: https://www.designsafe-ci.org/data/browser/public/
- NanoMine: https://materialsmine.org/wi/home
- NIST Materials Data Facility: https://www.materialsdatafacility.org/
- Awesome Materials Informatics: https://github.com/tilde-lab/awesome-materials-informatics
- PDEBench: https://github.com/pdebench/PDEBench and https://arxiv.org/abs/2210.07182 (datasets: advection, Burgers', diffusion-reaction, diffusion-sorption, compressible Navier-Stokes, incompressible Navier-Stokes, Darcy flow, shallow-water)
- PDEArena: https://github.com/microsoft/pdearena and https://arxiv.org/abs/2209.15616 (datasets: shallow water equations, velocity function formulation of Navier-Stokes equation)
- NVIDIA Modulus: https://developer.nvidia.com/modulus
Contact: E. Lejeune
If you are interested in listing your dataset, please get in touch! Last Updated: April 9, 2023