The 2025 Mechanical MNIST Challenge
Background
The original MNIST dataset set the standard for benchmarking classification algorithms with its curated set of handwritten digits. Inspired by this idea, Emma Lejeune (2020) created the Mechanical MNIST dataset, replacing pixels with material properties and digits with mechanically simulated deformation fields.
Building on this, we provide experimental data from 3D-printed MNIST-digit-inspired samples tested under mechanical load and analyzed via digital image correlation (DIC). The goal is to bridge data-driven modeling and experimental mechanics through two focused challenges.
Data Origin
We fabricated all samples using a Stratasys PolyJet J750 Digital Anatomy 3D printer (Stratasys, Eden Prairie, MN, USA), with geometries based on MNIST digits. Each sample measured 40 × 40 × 2 mm and contained a soft inclusion embedded in a stiffer surrounding matrix. To enable mechanical testing, we incorporated rigid clamps into the printed design.
After printing, we applied a speckle pattern for digital image correlation (DIC) and mounted the sample on a uniaxial tensile tester (Instron, Norwood, MA, USA). We extended each sample until failure while recording force and displacement using the Instron software. A custom LabVIEW program (National Instruments, Austin, TX, USA) captured synchronized images at 5 Hz during testing. We processed these images in DaVis (LaVision, Göttingen, Germany) to extract full-field displacement and strain data.
The Challenges
Challenge 1: Operator Learning
(Forward Problem)
Goal: Predict force-displacement curves and full-field strain maps using sample metadata and boundary conditions.
For training, participants will receive 90 data sets comprising full-field 2D DIC data, MNIST inclusion geometry, material annotations (matrix and inclusion), and prescribed boundary forces and displacements. Data will be provided for 189 frames per sample.
For testing, we will provide a [DOCKER BLA - FILL IN] and each participants’ submissions will be tested against 10 in-distribution data sets, 10 out-of-distribution data sets (not from the MNIST set), and 10 homogeneous material samples.
Challenge 2: Inverse Learning
(Inverse Problem)
Goal: Infer hidden geometry and material distribution from mechanical response.
For training, participants will receive 90 data sets comprising force-displacement curves and full-field 2D DIC strain maps, just as in Challenge 1.
For testing, we will test the participants’ submissions against the 10 in-distribution, 10 out-of-distribution, and 10 homogeneous samples.
Data Access
All datasets (STL files, force-displacement curves, strain maps) will be released on GitHub in June 2025.
GitHub Repository: Coming soon
Challenge Evaluation & Distribution
We will provide a submission portal closer to the submission date (see Timeline below). We will ask each participant to submit a Docker [FILL IN] so that we can evaluate each submission without revealing our test data. Based on our predefined success criteria, we will evaluate and rank each submission. Please note that our initial findings will be published blinded in a shared publication. Each participant will be a co-author on this first publication. Subsequently, we invite each participant to contribute to a special issue in [FILL IN] in which they discuss the details of their approach and evaluate its strength against the now accessible test data.
Organizers & Contact
Manuel Rausch (The University of Texas at Austin - manuel.rausch@utexas.edu)
Adrian Buganza Tepole (Columbia University - ab6035@columbia.edu)
Jan Fuhg (The University of Texas at Austin - jan.fuhg@utexas.edu)
Francisco Sahli Costabal (Pontificia Universidad Católica de Chile - fsc@ing.puc.cl)
Timeline
July 2025: Challenge launch & dataset released
August 2025: Submission portal opens
Feb 1, 2026: Submission deadline
March 2026: Blinded evaluation
April 2026: Zoom session with submitters
May–July 2026: Paper #1 (blinded results)
Fall 2026: Optional resubmissions, Paper #2