# pyALDIC: A Python Implementation of Augmented Lagrangian Digital Image Correlation with a GUI, Adaptive Meshing, and Mask-Aware Subset Splitting

arXiv: https://arxiv.org/abs/2607.22755 · HTML: https://mlsanity.com/paper/2607.22755/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 103 references that are in the map.
- First posted on arXiv 23 Jul 2026.

## Builds on (most cited first, 7 of 7)

- [Unsupervised discovery of interpretable hyperelastic constitutive laws](https://mlsanity.com/paper/2010.13496/) (2020)
- [A Physics-Guided Neural Operator Learning Approach to Model Biological Tissues from Digital Image Correlation Measurements](https://mlsanity.com/paper/2204.00205/) (2022)
- [Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review](https://mlsanity.com/paper/2303.07647/) (2023)
- [A comparative study of calibration techniques for finite strain elastoplasticity: Numerically-exact sensitivities for FEMU and VFM](https://mlsanity.com/paper/2503.19782/) (2025)
- [Inference of Heterogeneous Material Properties via Infinite-Dimensional Integrated DIC](https://mlsanity.com/paper/2408.10217/) (2024)
- [SerialTrack: ScalE and Rotation Invariant Augmented Lagrangian Particle Tracking](https://mlsanity.com/paper/2203.12573/) (2022)
- [Full-Field Calibration of Coupled Thermomechanical Material Models at Finite Strain](https://mlsanity.com/paper/2606.05465/) (2026)

## Abstract (arXiv)

pyALDIC is an open-source Python implementation of augmented Lagrangian digital image correlation (AL-DIC) for full-field displacement and strain measurement. The software combines a graphical user interface with a scriptable Python API and supports adaptive quadtree meshing, mask-aware subset splitting near cracks and holes, and selectable Local DIC and AL-DIC solver modes. Numba acceleration enables efficient analysis, while automated tests, documentation, and reproducible examples support reliable use acrossWindows, macOS, and Linux. Verification cases include synthetic displacement fields, rigid-body motion, Mode-I cracking, adaptive refinement, and experimental uniaxial tension. pyALDIC is distributed through PyPI, GitHub, and Zenodo under a BSD-3-Clause license for reproducibility. pyALDIC is openly available at https://github.com/zachtong/pyALDIC.