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REVIEW 3 major objections 4 minor 112 references

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

T0 review · 3 major / 4 minor · reviewed 2026-08-01 · deepseek-v4-flash

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Pith's one-line read pyALDIC is an open-source Python implementation of augmented Lagrangian digital image correlation, coupling local subset tracking to a global compatibility solve, with adaptive meshing and mask-aware subset splitting for strain measurement

desk verdict A well-engineered Python port of AL-DIC with a real gap: the paper never shows quantitative accuracy results, so the central claim that the software works correctly is currently more asserted than demonstrated. read the letter →

arxiv 2607.22755 v1 pith:XCPVUT2O submitted 2026-07-23 eess.IV cond-mat.mtrl-scics.CV

Zixiang Tong , Jin Yang This is my paper · ORCID
classification eess.IVcond-mat.mtrl-scics.CV
keywords digitalimagecorrelationaugmentedLagrangianfull-fieldstrainmeasurementadaptivequadtreemeshingmask-awaresubsetsplittingdiscontinuousdisplacementfieldsopen-sourcesoftwareADMM
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

pyALDIC is an open-source Python implementation of augmented Lagrangian digital image correlation (AL-DIC), a method for measuring full-field displacements and strains from speckled specimen images. The paper's claim is that this implementation makes the previously MATLAB-only AL-DIC formulation fully accessible: a graphical user interface and a scriptable Python API expose the same pipeline, and the package installs in one command on Windows, macOS, and Linux. Under the hood, the software couples per-subset correlation (IC-GN) to a global finite-element solve through an augmented Lagrangian / ADMM iteration, which suppresses noise and enforces kinematic compatibility, and it adds two features aimed at hard cases: adaptive quadtree meshing that concentrates nodes where error is high, and mask-aware subset splitting that keeps only the part of a correlation window lying on the same side of a crack or hole as the subset center. Verified on synthetic displacement fields, rigid-body motion, a Mode-I crack, adaptive refinement, and an experimental uniaxial tension test, the package is intended to give experimental mechanics researchers a reliable, reproducible, license-free tool for deformation measurement.

What carries the argument

The load-bearing object is the AL-DIC objective (Eq. 3 of the paper): a sum over subsets of the squared grayscale-intensity difference between reference and deformed images, augmented by penalty and Lagrange-multiplier terms that pull each subset's local displacement and gradient toward a single globally continuous finite-element field. Minimization proceeds by ADMM, alternating (1) per-subset IC-GN updates, (2) a global compatibility solve for the continuous field, and (3) updates of the scaled dual variables; turning off the global step reduces the method to plain local DIC, so one control switches between two solvers. Two supporting mechanisms carry the paper's claimed advantages: adaptiv

What would settle it

A synthetic Mode-I crack image pair with known ground-truth displacements, positioned so the crack passes directly through a row of subset centers; if the displacements measured at those nodes depart from the known crack-opening field beyond the method's own tolerance, the center-retention rule fails in the regime it is designed for. Re-running the paper's distributed Mode-I example with the mesh shifted by half a subset spacing would produce the same test: nodes whose centers the crack now crosses should be flagged unreliable, and neighboring readings should show a measurable error jump.

Watch

Extended reading notes

Core claim

The paper's central claim is that a single open-source codebase can faithfully implement the full AL-DIC workflow — local inverse-compositional Gauss-Newton (IC-GN) subset tracking, a global Q4 finite-element compatibility solve, and the augmented-Lagrangian coupling between them — while adding adaptive quadtree refinement and mask-aware subset splitting, and that this combination produces reliable full-field displacement and strain measurements where plain local DIC struggles: noisy images, steep displacement gradients, cracks, holes, and ROI boundaries. The load-bearing assertion is correctness of implementation. The paper supports it with verification cases spanning synthetic displacement

Load-bearing premise

The weakest premise is that each subset's center lies inside the material region whose motion is being measured: mask-aware splitting keeps only the connected valid component containing that center, so a crack passing through or immediately beside a center leaves a tiny, possibly misleading, or flagged-unreliable subset — exactly where the paper's claimed advantage matters most.

Editorial extensions

If this is right

  • Researchers without a MATLAB license can now run the validated AL-DIC formulation end-to-end on Windows, macOS, or Linux, in a GUI or in scripts, lowering the barrier to noise-robust full-field measurement.
  • Subsets near cracks and holes no longer mix pixels from independently moving regions, so displacement estimates on both faces of a discontinuity should be cleaner than with naive masked correlation.
  • The same codebase serves two regimes: plain local DIC for fast, smooth, low-noise fields and full AL-DIC when noise, gradients, or discontinuities demand global regularization, at a reported throughput penalty of roughly 2–6×.
  • Adaptive quadtree refinement concentrates correlation nodes where they are needed, so complex-geometry analyses can reach a given accuracy with fewer points than a uniform mesh.
  • The scriptable API and reproducible distributed examples let DIC be embedded in batch processing, notebook workflows, and downstream full-field model calibration.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the computational core is decoupled from the GUI, the same validated machinery could be retargeted to stereo-DIC and volumetric correlation — extensions the authors list as roadmap items — so the local-global coupling might generalize across measurement modalities rather than staying a 2D-image technique.
  • The roughly threefold memory reduction from chunked cross-correlation on a 4096×4096 image implies the practical image-size ceiling is much higher than before, which would make full-field measurement of large specimens or very high-resolution scans feasible on ordinary workstations.
  • A natural testable extension of the paper's own design is to benchmark mask-aware splitting against plain pixel-exclusion correlation on synthetic crack images with known ground truth, plotting error against the crack's distance from the subset center; that would delimit precisely where the claimed advantage over naive masking holds.
  • With its open data, scriptable API, and documented reference cases, the package gives learning-based DIC methods a reproducible, physics-grounded baseline to measure against — a role the paper notes its predecessor has already played in community evaluations.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper presents pyALDIC, an open-source Python implementation of augmented Lagrangian digital image correlation (AL-DIC), with a GUI, scriptable API, adaptive quadtree meshing, mask-aware subset splitting, and selectable Local DIC / AL-DIC solvers. The authors describe the software architecture, the AL-DIC formulation (Section 2.2.2, Eq. 3), algorithmic details (Algorithm 1), adaptive refinement, mask-aware splitting, seed-propagation initialization, performance measurements (Table 4), and distribution/verification artifacts. The central claim is that pyALDIC correctly implements the AL-DIC formulation and its advanced features so that users can obtain reliable full-field displacement and strain measurements. The manuscript, however, reports no quantitative accuracy results for the listed verification cases (synthetic, Mode-I crack, adaptive refinement, uniaxial tension); only throughput and memory usage are tabulated. The Conclusions (Section 3) attribute prior validation of the AL-DIC formulation and DIC Challenge 2.0 to the earlier MATLAB implementation, not to pyALDIC itself.

Significance. If the implementation is correct, pyALDIC fills a genuine gap: it is the first open-source, cross-platform Python package combining AL-DIC with adaptive meshing, mask-aware subset splitting, and a GUI, and its release under BSD-3-Clause with a scriptable API lowers barriers to reproducible DIC workflows. The paper's software-engineering strengths are substantial: more than 1,300 automated tests, continuous integration on Python 3.10–3.12, documented API/GUI, reproducible benchmark scripts, and distribution through PyPI/GitHub/Zenodo. These are concrete and checkable. However, the paper's load-bearing scientific claim—that pyALDIC yields reliable displacement and strain measurements—is currently supported only indirectly. The authors list verification cases but do not report errors, error maps, or comparisons to analytic ground truth or to the original MATLAB ALDIC implementation. The reported throughput (Table 4) validates speed, not accuracy. Because subtle implementation errors in IC-GN/ADMM, hanging-node constraints, or mask splitting would not be exposed by passing unit tests alone, the absence of quantitative validation leaves the central claim under-supported.

major comments (3)
  1. [Abstract; Section 1; Section 3; Data availability] The paper's central claim is that users can obtain reliable measurements from pyALDIC, but no quantitative accuracy results are reported for any of the verification cases listed in the Abstract and Data availability. Table 4 reports only throughput (POI·s−1). Section 3 states that 'the AL-DIC formulation's capability was evaluated ... using the DIC Challenge 2.0 datasets' [16], but that evaluation belongs to the earlier MATLAB ALDIC implementation, not to pyALDIC. The authors should add, for each listed verification case, a quantitative comparison against a ground truth or an established reference: e.g., root-mean-square and maximum displacement errors for the synthetic benchmark, crack-opening displacement profile for the Mode-I example, convergence of adaptive refinement, and a strain comparison for the uniaxial tension experiment against an extensometer or a published result. Without
  2. [Section 2.2.2, Mask-aware subset splitting] The mask-aware splitting procedure retains only the connected valid component containing the subset center. This assumes the subset center lies in the material region whose motion is to be measured. If a crack passes through or very near the subset center, the retained component can be small, disconnected from the intended material region, or the wrong component. The flagged-unreliable criterion (<50% pixels) may help, but the manuscript does not demonstrate how the method behaves in that regime. The distributed Mode-I example is mentioned but not quantified. Please add a sensitivity analysis or at least show the retained pixel fraction and displacement error for subsets near the crack tip, where the assumption is most stressed.
  3. [Section 2.2.2, Adaptive quadtree mesh refinement; Algorithm 1] The adaptive quadtree refinement introduces hanging-node constraints for midside degrees of freedom, and the global solve in Algorithm 1 is stated to operate on 'M'. However, no verification is shown that the hanging-node constraints are correctly implemented: e.g., a patch test, convergence of strain under refinement, or a comparison of an adaptively refined mesh against a uniformly refined mesh for the same synthetic displacement field. The adaptive-mesh verification case is listed in Data availability but not analyzed. This is load-bearing because an incorrect constraint would silently corrupt the displacement field near refinement boundaries.
minor comments (4)
  1. [Section 2.2.2, GUI/API subset size convention] The GUI/API difference (odd 2h+1 vs even 2h) is explained, but it would help to state the convention consistently in Table 4 and in the algorithm description to avoid user confusion when replicating benchmarks.
  2. [Table 3] The table entries for Ncorr, DICe, and other packages use a simplified checkmark format, but the legend is incomplete and the 'partial' notation is only defined in the caption. Consider adding a full legend below the table.
  3. [Acknowledgements / Conflict of interest] The conflict-of-interest statement says 'no significant financial support' while the Acknowledgements list NSF and ONR grants. This could be seen as inconsistent; rephrase to clarify that funding sources had no influence.
  4. [Section 2.2.3, Performance] The throughput numbers depend on representative settings (subset size, spacing, number of ADMM iterations). It would be useful to report the number of ADMM iterations used per image size and whether convergence tolerance ε was identical across sizes, so the performance comparison is reproducible.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: pyALDIC's implementation claims are not predictions derived from fitted inputs; the cited AL-DIC formulation is prior work and the validation gap is evidential, not circular.

full rationale

This is a software-description paper rather than a derivation of new physical results. The paper explicitly states that 'The AL-DIC formulation itself was introduced previously [14,17]; the principal contributions of the present work are its accessible software implementation...' (Section 1), so importing Eqs. (1)-(3) and Algorithm 1 from Ref. [14] is a transparent dependency, not a prediction forced by construction. No fitted parameter is renamed as a verification result; the quantitative results that are reported (Table 4 throughput, memory reduction, GUI/API availability, >1,300 tests) are about the code artifact and are not used to infer the quantities they claim to measure. The central correctness claim is left under-supported because the paper only lists verification cases ('Verification cases include synthetic displacement fields, rigid-body motion, Mode-I cracking, adaptive refinement, and experimental uniaxial tension') and states that 'Runnable examples, together with the corresponding datasets and analysis scripts, are distributed' without presenting error metrics or comparisons; however, an omitted quantitative validation is a support/correctness gap, not a circular reduction. The self-citations in the Conclusions ('The AL-DIC formulation's capability was evaluated ... [16]') refer to prior community and author benchmarks of the method, which is legitimate context; they are not used to derive this software's behavior by construction. There is no exhibit of Eq. X = Eq. Y by definition, and no fitted-input-called-prediction step, so the circularity score is 0.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

No new physical entities are introduced. Free parameters are algorithm hyperparameters (penalty weights, tolerance, refinement thresholds, mask-fraction threshold) whose values are not reported in the paper; they affect behavior but are not fitted to a scientific claim.

free parameters (4)
  • Penalty parameters β and μ = not stated in paper (defaults set in code)
    In Eq. (3), β and μ control the coupling strength between local and global fields; chosen by software defaults or user, affect convergence and regularization but are not fitted to a scientific target.
  • Mask-valid fraction threshold = 0.5 (one-half)
    A node is flagged unreliable if the retained connected component has fewer than half the pixels of the original subset (Section 2.2.2); threshold chosen by hand.
  • ADMM tolerance ε and max iterations k_max = not stated
    Algorithm 1 convergence criteria chosen by software defaults; affect accuracy and speed.
  • Adaptive refinement error threshold and Light/Medium/Strong presets = not stated
    Section 2.2.2 adaptive refinement; thresholds chosen by hand for the a posteriori IC-GN residual criterion.
assumptions (5)
  • domain assumption AL-DIC formulation of Ref [14] is correct, including the augmented Lagrangian objective (Eq. 3) and ADMM iterations (Algorithm 1).
    pyALDIC implements this formulation directly and does not re-derive it; any error in the prior derivation would propagate.
  • domain assumption IC-GN local optimization converges to a good subset displacement solution (Refs [4,5]).
    The local update in Subproblem 1 relies on IC-GN convergence.
  • domain assumption FFT-based cross-correlation and seed propagation provide adequate initial guesses.
    Initial guess step (Section 2.2.1 and Algorithm 1 Step 2) assumes these heuristics avoid local minima.
  • domain assumption The distributed verification examples (synthetic displacement, Mode-I crack, adaptive refinement, uniaxial tension) have valid ground truth or expected behavior and were run with the shipped version.
    The paper asserts verification but shows no results; the claim rests on the repository artifacts.
  • domain assumption ADMM with scaled dual variables converges for the correlated intensity objective within the stated iteration cap.
    Algorithm 1 assumes convergence of the alternating scheme; inherited from Refs [14,15].

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Cite this review

Pith. "Pith review of pyALDIC: A Python Implementation of Augmented Lagrangian Digital Image Correlation with a GUI, Adaptive Meshing, and Mask-Aware Subset Splitting." pith.science (2026). https://pith.science/paper/XCPVUT2O

@misc{pith2026260722755,
  author       = {Pith},
  title        = {Pith review of: pyALDIC: A Python Implementation of Augmented Lagrangian Digital Image Correlation with a GUI, Adaptive Meshing, and Mask-Aware Subset Splitting},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XCPVUT2O}},
  note         = {Machine review of arXiv:2607.22755}
}
read the original abstract

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.

Figures

Figures reproduced from arXiv: 2607.22755 by the authors.

Figure 1
Figure 1. pyALDIC processing workflow. An image sequence with optional masks passes through ROI editing, pa [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Screenshot of the pyALDIC desktop GUI (v0.6.0) during ROI definition on the reference image of a [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Adaptive quadtree refinement through two successive refinement levels. [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Consequently, a subset adjacent to a crack correlates only the speckle pattern located [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]
Figure 4
Figure 4. Figure 4: Mask-aware subset splitting near a displacement discontinuity. (a) Correlation subsets near a mask [PITH_FULL_IMAGE:figures/full_fig_p011_4.png]
Figure 5
Figure 5. Figure 5: Seed-propagation initial-displacement estimation for a cracked specimen. (a) Initialization from a user [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]

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Reference graph

Works this paper leans on

112 extracted references · 2 linked inside Pith

  1. [16]

    DIC Challenge 2.0: Developing Images and Guidelines for Evaluating Accuracy and Resolution of 2D Analyses.Experimental Mechanics, pages 1–16, 2022

    P L Reu, Benoit Blaysat, Edward Andò, Kaushik Bhattacharya, Cyrille Couture, Vincent Couty, Debasis Deb, S S Fayad, M A Iadicola, Stéphanie Jaminion, et al. DIC Challenge 2.0: Developing Images and Guidelines for Evaluating Accuracy and Resolution of 2D Analyses.Experimental Mechanics, pages 1–16, 2022

    2022

  2. [1]

    Sutton, Jean-José Orteu, and Hubert W

    Michael A. Sutton, Jean-José Orteu, and Hubert W. Schreier.Image Correlation for Shape, Motion and Deformation Measurements: Basic Concepts, Theory and Applications. Springer, New York, 2009

    2009

  3. [2]

    Digital image correlation for surface deformation measurement: historical developments, recent advances and future goals.Measurement Science and Technology, 29(8):082001, 2018

    Bing Pan. Digital image correlation for surface deformation measurement: historical developments, recent advances and future goals.Measurement Science and Technology, 29(8):082001, 2018

    2018

  4. [3]

    Elizabeth M. C. Jones and Mark A. Iadicola, editors.A Good Practices Guide for Digital Image Correlation. International Digital Image Correlation Society (iDICs), 1st edition, 2018

    2018

  5. [4]

    Lucas-Kanade 20 years on: A unifying framework

    Simon Baker and Iain Matthews. Lucas-Kanade 20 years on: A unifying framework. International Journal of Computer Vision, 56(3):221–255, 2004

    2004

  6. [5]

    Fast, robust and accurate digital image correlation calculation without redundant computations.Experimental Mechanics, 53(7):1277–1289, 2013

    Bing Pan, Kai Li, and Wei Tong. Fast, robust and accurate digital image correlation calculation without redundant computations.Experimental Mechanics, 53(7):1277–1289, 2013

    2013

  7. [6]

    Combining image compression with digital image correlation.Experimental Mechanics, 59(5):629–642, 2019

    Jin Yang and Kaushik Bhattacharya. Combining image compression with digital image correlation.Experimental Mechanics, 59(5):629–642, 2019

    2019

  8. [7]

    Estimation of surface and interface strains in deformation processing using an ensemble averaged digital image correlation method

    Deepika Gupta and Koushik Viswanathan. Estimation of surface and interface strains in deformation processing using an ensemble averaged digital image correlation method. Journal of Manufacturing Processes, 120:86–95, 2024

    2024

Show all 112 references
  1. [8]

    subset splitting

    J. Poissant and F. Barthelat. A novel “subset splitting” procedure for digital image corre- lation on discontinuous displacement fields.Experimental Mechanics, 50(3):353–364, 2010

    2010

  2. [9]

    PF-DIC: Phase field digital image correlation for in- tegrated full-field displacement, strain, and damage measurements.arXiv preprint arXiv:2606.16850, 2026

    Dingxiang Zhu and Ye Lu. PF-DIC: Phase field digital image correlation for in- tegrated full-field displacement, strain, and damage measurements.arXiv preprint arXiv:2606.16850, 2026

    2026

  3. [10]

    Active slip system identification in polycrystalline metals by digital image correlation (DIC).Experimental Mechanics, 57(1):115–127, 2017

    Z Chen and SH Daly. Active slip system identification in polycrystalline metals by digital image correlation (DIC).Experimental Mechanics, 57(1):115–127, 2017

    2017

  4. [11]

    Ran Ni, Carl J Boehlert, Xianhua Zheng, Yaming Ran, Saijun Huang, Ying Zeng, Jiang Zheng, Qudong Wang, Hao Zhou, and Dongdi Yin. Revisiting tension-compression asymmetry in a Mg alloy: insights from statistical strain partitioning and intra-/inter- granular mechanisms at the n...

    2025

  5. [12]

    A comparative study of tension and compression in pure Mg sheet: Unveiling nanoscale intra-and inter-granular accommodation mechanisms via HRDIC

    Yaming Ran, Yuanshuai Zhang, Shen Hua, Jiang Zheng, Qudong Wang, Hao Zhou, Ran Ni, and Dongdi Yin. A comparative study of tension and compression in pure Mg sheet: Unveiling nanoscale intra-and inter-granular accommodation mechanisms via HRDIC. Journal of Materials Science & T...

    2026

  6. [13]

    Full-field ultrahigh-speed quantification of dy- namic shear ruptures using digital image correlation.Experimental Mechanics, 59(5):551– 582, 2019

    V Rubino, AJ Rosakis, and N Lapusta. Full-field ultrahigh-speed quantification of dy- namic shear ruptures using digital image correlation.Experimental Mechanics, 59(5):551– 582, 2019

    2019

  7. [14]

    Augmented Lagrangian digital image correlation.Experi- mental Mechanics, 59(2):187–205, 2019

    J Yang and K Bhattacharya. Augmented Lagrangian digital image correlation.Experi- mental Mechanics, 59(2):187–205, 2019

    2019

  8. [15]

    Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipli- ers.Foundations and Trends in Machine Learning, 3(1):1–122, 2011

    Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato, and Jonathan Eckstein. Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipli- ers.Foundations and Trends in Machine Learning, 3(1):1–122, 2011

    2011

  9. [17]

    Yang and K

    J. Yang and K. Bhattacharya. Fast adaptive mesh augmented Lagrangian digital image correlation.Experimental Mechanics, 61(4):719–735, 2021

    2021

  10. [18]

    Fast adaptive global digital image correlation

    Jin Yang and Kaushik Bhattacharya. Fast adaptive global digital image correlation. In Advancement of Optical Methods & Digital Image Correlation in Experimental Mechanics, Vol- ume 3: Proceedings of the 2018 Annual Conference on Experimental and Applied Mechanics, pages 69–73....

    2018

  11. [19]

    J Yang, V Rubino, Z Ma, JL Tao, Y Yin, A McGhee, WX Pan, and C Franck. SpatioTem- porally Adaptive Quadtree mesh (STAQ) Digital Image Correlation for resolving large deformations around complex geometries and discontinuities.Experimental Mechanics, 2022

    2022

  12. [20]

    Machine learning-aided spatial adaptation for improved digital image correlation anal- ysis of complex geometries.Strain, 62(1):e70022, 2026

    Jeffrey Leu, Zixiang Tong, Andrew Doty, Solon Tsimpoukis, Bolei Deng, and Jin Yang. Machine learning-aided spatial adaptation for improved digital image correlation anal- ysis of complex geometries.Strain, 62(1):e70022, 2026

    2026

  13. [21]

    J Yang, L Hazlett, A. K. Landauer, and C Franck. Augmented Lagrangian Digital Volume Correlation (ALDVC).Experimental Mechanics, 60(9):1205–1223, 2020

    2020

  14. [22]

    Rausch, Yujie Zhang, Virginia Knight, and Jin Yang

    Zixiang Tong, Danila Frolkin, Hongyang Shi, LaRue Trace, Manuel K. Rausch, Yujie Zhang, Virginia Knight, and Jin Yang. 3D Stereo Adaptive Mesh Augmented Lagrangian Digital Image Correlation.Experimental Mechanics, 65:1387–1411, 2025

    2025

  15. [23]

    Blaber, B

    J. Blaber, B. Adair, and A. Antoniou. Ncorr: Open-Source 2D Digital Image Correlation Matlab Software.Experimental Mechanics, 55(6):1105–1122, 2015

    2015

  16. [24]

    D. Z. Turner, R. B. Lehoucq, P . L. Reu, N. Garavito-Camargo, et al. DICe: Digital Image Correlation Engine. Sandia National Laboratories, 2015

    2015

  17. [25]

    S. N. Olufsen, M. E. Andersen, and E. Førre. µdic: An open-source toolkit for digital image correlation.SoftwareX, 11:100391, 2020

    2020

  18. [26]

    Opencorr gui: A free software for accurate and robust deformation measurement of digital image correlation and digital volume correlation.Available at SSRN 7088402

    Rui Li, Kai He, Haoqiang Ren, Hongli Wang, Yajing Zhang, Chang Peng, Yifei Zhou, Taolin Sun, Liqun Tang, and Zhenyu Jiang. Opencorr gui: A free software for accurate and robust deformation measurement of digital image correlation and digital volume correlation.Available at SSR...

  19. [27]

    iCorrVision-2D: An integrated python-based open-source digital image correlation software for in-plane measurements (Part 1).SoftwareX, 19:101131, 2022

    João Carlos Andrade de Deus Filho, Luiz Carlos da Silva Nunes, and José Manuel Car- doso Xavier. iCorrVision-2D: An integrated python-based open-source digital image correlation software for in-plane measurements (Part 1).SoftwareX, 19:101131, 2022. 15

    2022

  20. [28]

    UFreckles, 2018

    Julien Réthoré. UFreckles, 2018. Open-source MATLAB global (FE-based) DIC/DVC with GUI

    2018

  21. [29]

    YaDICs: Yet another digital image correlation software.http://yadics.univ-lille1. fr/. Open-source C++DIC (University of Lille); accessed 2026-07-08

    2026

  22. [30]

    Open- source Python subset-based DIC; accessed 2026-07-08

    PReDIC: Python digital image correlation.https://github.com/texM/PReDIC. Open- source Python subset-based DIC; accessed 2026-07-08

    2026

  23. [31]

    pyALDIC: Augmented Lagrangian Digital Image Correla- tion in Python

    Zixiang Tong and Jin Yang. pyALDIC: Augmented Lagrangian Digital Image Correla- tion in Python. Zenodo software archive, version v0.6.0, 2026.https://doi.org/10. 5281/zenodo.19521061

    2026

  24. [32]

    Automated analysis framework of strain partitioning and deformation mechanisms via multimodal fusion and computer vision.International Journal of Plasticity, 182:104119, 2024

    Ran Ni, Carl J Boehlert, Ying Zeng, Bo Chen, Saijun Huang, Jiang Zheng, Hao Zhou, Qudong Wang, and Dongdi Yin. Automated analysis framework of strain partitioning and deformation mechanisms via multimodal fusion and computer vision.International Journal of Plasticity, 182:104119, 2024

    2024

  25. [33]

    Temperature-dependent interplay of intra-and inter- granular deformation mechanisms in Mg-10Y: Statistical analysis from an HRDIC per- spective.Acta Materialia, 296:121256, 2025

    Ran Ni, Carl J Boehlert, Bo Chen, Yuanshuai Zhang, Ying Zeng, Jiang Zheng, Hao Zhou, Qudong Wang, and Dongdi Yin. Temperature-dependent interplay of intra-and inter- granular deformation mechanisms in Mg-10Y: Statistical analysis from an HRDIC per- spective.Acta Materialia, 29...

    2025

  26. [34]

    Residual intensity as a morphological identifier of twinning fields in microscopic image correlation.Experimental Mechanics, 61(3):499–514, 2021

    NA Özdür, IB Üçel, J Yang, and CC Aydıner. Residual intensity as a morphological identifier of twinning fields in microscopic image correlation.Experimental Mechanics, 61(3):499–514, 2021

    2021

  27. [35]

    Electric current-driven heteroge- neous microstructures in dual-phase titanium alloys.Nature Communications, 17(1):3470, 2026

    Shaojie Gu, Yasuhiro Kimura, Yi Cui, Yasuyuki Morita, Sora Isoi, Chang Liu, Xinming Yan, Bingfeng Ju, Huayong Yang, Yuhki Toku, et al. Electric current-driven heteroge- neous microstructures in dual-phase titanium alloys.Nature Communications, 17(1):3470, 2026

    2026

  28. [36]

    Automated analysis framework of strain partitioning and de- formation mechanisms via hrdic-ebsd fusion and computer vision: Application to a Mg alloy.Available at SSRN 4836423

    Ran Ni, Carl J Boehlert, Ying Zeng, Bo Chen, Saijun Huang, J Zheng, Hao Zhou, Qudong Wang, and Dongdi Yin. Automated analysis framework of strain partitioning and de- formation mechanisms via hrdic-ebsd fusion and computer vision: Application to a Mg alloy.Available at SSRN 4836423

  29. [37]

    Mapping the strain-localization evolution of grain boundary and its interactions with slip/twin at the microscale.Journal of Magnesium and Alloys, 2025

    Ran Ni, Saijun Huang, Lingling Fan, Kang Wei, Ying Zeng, Jiang Zheng, Qudong Wang, Hao Zhou, and Dongdi Yin. Mapping the strain-localization evolution of grain boundary and its interactions with slip/twin at the microscale.Journal of Magnesium and Alloys, 2025

    2025

  30. [38]

    Statistical assessment of slip transfer in a Mg alloy: insights from nano-scale deformation-field continuity via HRDIC.Available at SSRN 5993534

    Yuanshuai Zhang, Carl J Boehlert, Ran Ni, Bo Chen, Ying Zeng, J Zheng, Qudong Wang, Hao Zhou, and Dongdi Yin. Statistical assessment of slip transfer in a Mg alloy: insights from nano-scale deformation-field continuity via HRDIC.Available at SSRN 5993534

  31. [39]

    Mapping precipitate-slip/twin interactions in Mg-Sn alloys: Insights from strain partitioning via HRDIC-EBSD multimodal analysis.Scripta Materialia, 277:117227, 2026

    Siyu He, Ying Zeng, Meilin Huang, Ran Ni, Yuanshuai Zhang, Bin Jiang, and Dongdi Yin. Mapping precipitate-slip/twin interactions in Mg-Sn alloys: Insights from strain partitioning via HRDIC-EBSD multimodal analysis.Scripta Materialia, 277:117227, 2026

    2026

  32. [40]

    Oxygen effect on deformation mechanism and mechanical behavior of pure Ti at 77K.Acta Materialia, page 122433, 2026

    Jiajun Hu, Dongmei Zhang, Yonghao Zhao, Arnaud Caron, Yuanshuai Zhang, Shuaizhuo Wang, Bo Gao, Lirong Xiao, Dongdi Yin, Hao Zhou, et al. Oxygen effect on deformation mechanism and mechanical behavior of pure Ti at 77K.Acta Materialia, page 122433, 2026. 16

    2026

  33. [41]

    Detection of the onset of yielding and creep failure from digital image correlation.Physical Review Materials, 6(10):103601, 2022

    Tero Mäkinen, Agata Zaborowska, Małgorzata Frelek-Kozak, Iwona Jó´ zwik, Łukasz Kurpaska, Stefanos Papanikolaou, and Mikko J Alava. Detection of the onset of yielding and creep failure from digital image correlation.Physical Review Materials, 6(10):103601, 2022

    2022

  34. [42]

    Venkat Vivek Pamarthi, Tianzhu Sun, Abhishek Das, and Pasquale Franciosa. Tailoring the weld microstructure to prevent solidification cracking in remote laser welding of aa6005 aluminium alloys using adjustable ringmode beam.journal of Materials Research and Technology, 25:715...

    2023

  35. [43]

    Cyclic behaviours of superelastic shape-memory al- loy plates joined by tungsten inert gas welding.Construction and Building Materials, 402:132768, 2023

    Zhi-Peng Chen and Songye Zhu. Cyclic behaviours of superelastic shape-memory al- loy plates joined by tungsten inert gas welding.Construction and Building Materials, 402:132768, 2023

    2023

  36. [44]

    Oussama Khouchani, Anass Harmal, Tahar El-Korchi, Mingjiang Tao, and Harold W Walker. Effect of cellulose nanocrystals on performance of PVA fiber-reinforced geopoly- mer composites: Reaction kinetics, bending behavior, and toughening mechanisms.Con- struction and Building Mat...

    2024

  37. [45]

    Universal characteristics of local strain fields for creep failure prediction

    Bakhtiyar Mammadli, Tero Mäkinen, Karol Frydrych, Panagiotis G Asteris, and Stefanos Papanikolaou. Universal characteristics of local strain fields for creep failure prediction. International Journal of Mechanical Sciences, page 110612, 2025

    2025

  38. [46]

    Comparative study of strain using 2d Digital Image Correlation and extensometer on glass fiber

    Abhilash Salian and TP Ashwini. Comparative study of strain using 2d Digital Image Correlation and extensometer on glass fiber. InSmart Sensors Measurements and Instru- mentation: Select Proceedings of CISCON 2020, pages 351–366. Springer, 2021

    2020

  39. [47]

    A multiscale modeling approach for progressive dam- age analysis of notched ceramic matrix composites

    James Roach and Dianyun Zhang. A multiscale modeling approach for progressive dam- age analysis of notched ceramic matrix composites. InAIAA SCITECH 2025 Forum, page 1361, 2025

    2025

  40. [48]

    De- sign exploration of additively manufactured chiral auxetic structure using explainable machine learning.Materials & Design, 232:112128, 2023

    Ondrej Jirousek, Pramudita Satria Palar, Jan Falta, Yohanes Bimo Dwianto, et al. De- sign exploration of additively manufactured chiral auxetic structure using explainable machine learning.Materials & Design, 232:112128, 2023

    2023

  41. [49]

    Dis- covering chiral auxetic structures with near-zero Poisson’s ratio using an active learning strategy.Materials & Design, 244:113133, 2024

    Ondrej Jirousek, Jan Falta, Yohanes Bimo Dwianto, Pramudita Satria Palar, et al. Dis- covering chiral auxetic structures with near-zero Poisson’s ratio using an active learning strategy.Materials & Design, 244:113133, 2024

    2024

  42. [50]

    Experimen- tal Evaluation of the Effects of Discrete-Grading-Induced Discontinuities on the Material Properties of Functionally Graded Ti-6Al-4V Lattices.Materials, 17(4):822, 2024

    Junyang Ye, Ata Babazadeh-Naseri, C Fred Higgs III, and Benjamin J Fregly. Experimen- tal Evaluation of the Effects of Discrete-Grading-Induced Discontinuities on the Material Properties of Functionally Graded Ti-6Al-4V Lattices.Materials, 17(4):822, 2024

    2024

  43. [51]

    Tensile property measurement of lattice structures

    AT Fry, LE Crocker, MJ Lodeiro, M Poole, P Woolliams, A Koko, N Leung, D England, and C Breheny. Tensile property measurement of lattice structures. 2023

    2023

  44. [52]

    Strain-rate dependent compressive properties of inverted honeycomb lattice and bulk cylindrical samples 3D printed by MSLA method

    Jan Falta and Yunus Emre Yulmaz. Strain-rate dependent compressive properties of inverted honeycomb lattice and bulk cylindrical samples 3D printed by MSLA method. In27/28th Internatinoal Conference Engineering Mechsnics, pages 93–96, 2022

    2022

  45. [53]

    Characterization of stress localization in epoxy materials using augmented Lagrangian digital image correlation

    Ming Li and Ali Al Bataineh. Characterization of stress localization in epoxy materials using augmented Lagrangian digital image correlation. InInternational Conference on WorldS4, pages 191–198. Springer, 2025. 17

    2025

  46. [54]

    Open source, in-situ, intermediate strain-rate tensile impact device for soft materials and cell culture systems.Experimental Mechanics, 63(9):1445–1460, 2023

    Luke Summey, Jing Zhang, AK Landauer, Jamie Sergay, Jin Yang, Annalise Daul, Jialiang Tao, Jessica Park, A McGhee, and C Franck. Open source, in-situ, intermediate strain-rate tensile impact device for soft materials and cell culture systems.Experimental Mechanics, 63(9):1445–...

    2023

  47. [55]

    Toughening starch-based bioplastics with soy amyloid fibrils produced from tofu wastewater.ACS Sustainable Chemistry & Engineering, 13(21):8068–8077, 2025

    Shrestha Roy Goswami, Svitlana Mykolenko, Xiang Kong, and Raffaele Mezzenga. Toughening starch-based bioplastics with soy amyloid fibrils produced from tofu wastewater.ACS Sustainable Chemistry & Engineering, 13(21):8068–8077, 2025

    2025

  48. [56]

    Quantification of errors in applying DIC to fiber networks imaged by confocal microscopy.Experimental Mechanics, 62(7):1175–1189, 2022

    M Sarkar and J Notbohm. Quantification of errors in applying DIC to fiber networks imaged by confocal microscopy.Experimental Mechanics, 62(7):1175–1189, 2022

    2022

  49. [57]

    Evaluation of an inverse method for quan- tifying spatially variable mechanics.Journal of Biomechanical Engineering, 146(12):121006, 2024

    Daniel P Pearce and Colleen M Witzenburg. Evaluation of an inverse method for quan- tifying spatially variable mechanics.Journal of Biomechanical Engineering, 146(12):121006, 2024

    2024

  50. [58]

    Experimental approach for character- izing the nonlinear, time and temperature-dependent constitutive response of open-cell polyurethane foams.Strain, 60(6):e12478, 2024

    Jialiang Tao, Xiangyu Sun, and Christian Franck. Experimental approach for character- izing the nonlinear, time and temperature-dependent constitutive response of open-cell polyurethane foams.Strain, 60(6):e12478, 2024

    2024

  51. [59]

    Vaginal biomechanical function in premenopausal and postmenopausal women with and without pelvic organ prolapse.Scientific Reports, 15(1):27039, 2025

    Qinhan Zhou, Guang Li, Jasmine X Kiley, Benard Ogola, Elvis K Danso, Lynd- sey Buckner Baiamonte, Laurephile Desrosiers, Leise R Knoepp, Sarah H Lindsey, Maria E Florian-Rodriguez, et al. Vaginal biomechanical function in premenopausal and postmenopausal women with and without...

    2025

  52. [60]

    Smart digital image correlation patterns via 3D printing

    J Yang, JL Tao, and C Franck. Smart digital image correlation patterns via 3D printing. Experimental Mechanics, 61(7):1181–1191, 2021

    2021

  53. [61]

    High-speed, full-field deformation measurements near inertial microcavitation bubbles inside viscoelastic hydrogels.Experimental Mechanics, 63(1):63–78, 2023

    A McGhee, J Yang, EC Bremer, Z Xu, HC Cramer III, JB Estrada, DL Henann, and CJEM Franck. High-speed, full-field deformation measurements near inertial microcavitation bubbles inside viscoelastic hydrogels.Experimental Mechanics, 63(1):63–78, 2023

    2023

  54. [62]

    Inertial interface cavitation creates complex, flow-like structures within a soft solid.Experimental Mechanics, pages 1–19, 2026

    Jin Yang, Alexander McGhee, Zixiang Tong, Griffin Radtke, Mauro Rodriguez Jr, and Christian Franck. Inertial interface cavitation creates complex, flow-like structures within a soft solid.Experimental Mechanics, pages 1–19, 2026

    2026

  55. [63]

    Spatiotemporally-resolved kinematic and stress measurements of interfacial cavitation in soft matter via DIC

    Jin Yang, Alexander McGhee, Zixiang Tong, Lehu Bu, Sicong Wang, Griffin Radtke, Mauro Rodriguez, and Christian Franck. Spatiotemporally-resolved kinematic and stress measurements of interfacial cavitation in soft matter via DIC. InComputer Vision & Laser Vibrometry, volume 6, ...

    2026

  56. [64]

    High-speed, full-field mea- surement of large deformations near needle-induced cavitation bubbles within biologi- cal soft materials

    Lehu Bu, Zhao-Bang Hou, Sophie Polidoro, and Jin Yang. High-speed, full-field mea- surement of large deformations near needle-induced cavitation bubbles within biologi- cal soft materials. InSEM Annual Conference and Exposition on Experimental and Applied Mechanics, pages 115–...

    2024

  57. [65]

    Machine learning extraction of vis- coelastic material properties from full-field deformation measurements.Journal of the Mechanics and Physics of Solids, page 106589, 2026

    Congjie Wei, Lehu Bu, Jin Yang, and Chenglin Wu. Machine learning extraction of vis- coelastic material properties from full-field deformation measurements.Journal of the Mechanics and Physics of Solids, page 106589, 2026

    2026

  58. [66]

    Bioinspired brick-and-mortar geopolymer composites with ultra-high tough- ness.Cement and Concrete Composites, 137:104944, 2023

    Anass Harmal, Oussama Khouchani, Tahar El-Korchi, Mingjiang Tao, and Harold W Walker. Bioinspired brick-and-mortar geopolymer composites with ultra-high tough- ness.Cement and Concrete Composites, 137:104944, 2023

    2023

  59. [67]

    The impact of top-layer sliced lamella thickness and core type on surface-checking in engineered wood flooring.Forests, 14(11):2250, 2023

    Victor Grubîi and Jimmy Johansson. The impact of top-layer sliced lamella thickness and core type on surface-checking in engineered wood flooring.Forests, 14(11):2250, 2023. 18

    2023

  60. [68]

    Victor Grubîi, Jimmy Johansson, and Ola Dagbro. Measurement of surface-checking in sliced lamellae-based engineered wood flooring using digital image correlation.Euro- pean Journal of Wood and Wood Products, 81(6):1427–1436, 2023

    2023

  61. [69]

    The Influence of Slicing Thickness on the Perpen- dicular to Grain Tensile Properties of Oak (Quercus robur L

    Victor Grubîi and Jimmy Johansson. The Influence of Slicing Thickness on the Perpen- dicular to Grain Tensile Properties of Oak (Quercus robur L. and Quercus petraea L.) Lamellae.Applied Sciences, 13(22):12254, 2023

    2023

  62. [70]

    Mechanical properties of basalt: a study on compressive loading at different strain rates using SHPB

    Jan Falta, Nela Krˇ cmᡠrová, Tomáš Fíla, Martin Vavro, and Leona Vavro. Mechanical properties of basalt: a study on compressive loading at different strain rates using SHPB. Acta Polytechnica CTU Proceedings, 42:17–21, 2023

    2023

  63. [71]

    PhD thesis, Linnaeus University Press, 2023

    Victor Grubîi.Quality Aspects of Sliced Oak Lamellae in Development of Engineered Wood Flooring. PhD thesis, Linnaeus University Press, 2023

    2023

  64. [72]

    Uniaxial tensile stress relaxation and cracking behavior of shccs incorporating blast furnace slag and fly ash

    Faizudin Hafiz Zadah and Yao Luan. Uniaxial tensile stress relaxation and cracking behavior of shccs incorporating blast furnace slag and fly ash. InBiennial RILEM Youth Symposium on Building Materials and Construction, pages 909–923. Springer, 2025

    2025

  65. [73]

    Deformation measurement within lithium-ion battery using sparse- view computed tomography and digital image correlation.Measurement Science and Tech- nology, 34(2):025402, 2023

    Yapeng Wu, Liang Sun, Xiangchun Zhang, Min Yang, Dalong Tan, Chao Hai, Jing Liu, and Juntao Wang. Deformation measurement within lithium-ion battery using sparse- view computed tomography and digital image correlation.Measurement Science and Tech- nology, 34(2):025402, 2023

    2023

  66. [74]

    Design and characterization of an efficient multistable push-pull linear actuator using magnetic shape memory alloys.IEEE Access, 12:107855– 107871, 2024

    Robert Courant and Jürgen Maas. Design and characterization of an efficient multistable push-pull linear actuator using magnetic shape memory alloys.IEEE Access, 12:107855– 107871, 2024

    2024

  67. [75]

    Pattern evolution and modal decomposition of faraday waves in a brimful cylinder.Journal of Fluid Mechanics, 974:A56, 2023

    Shimin Zhang, Alistair GL Borthwick, and Zhiliang Lin. Pattern evolution and modal decomposition of faraday waves in a brimful cylinder.Journal of Fluid Mechanics, 974:A56, 2023

    2023

  68. [76]

    Transitional response of double-mode faraday waves in a brimful container.Physical Review Fluids, 10(3):034003, 2025

    Shimin Zhang and Zhiliang Lin. Transitional response of double-mode faraday waves in a brimful container.Physical Review Fluids, 10(3):034003, 2025

    2025

  69. [77]

    In- fluence of displacement gradients on laser speckle photography

    Schweickhardt León, Tausendfreund Andreas, Stöbener Dirk, and Fischer Andreas. In- fluence of displacement gradients on laser speckle photography. InEPJ Web of Confer- ences, volume 266, page 10020. EDP Sciences, 2022

    2022

  70. [78]

    Free-surface topog- raphy measurements of fluid layers over a smoothly varying bed.Experiments in Fluids, 66(11):199, 2025

    Shimin Zhang, Hao Yang, Alistair GL Borthwick, and Zhiliang Lin. Free-surface topog- raphy measurements of fluid layers over a smoothly varying bed.Experiments in Fluids, 66(11):199, 2025

    2025

  71. [79]

    R 3-DICnet: an end-to-end recursive residual refinement DIC network for larger deformation measurement.Optics Express, 32(1):907–921, Jan 2024

    Jiashuai Yang, Kemao Qian, and Lianpo Wang. R 3-DICnet: an end-to-end recursive residual refinement DIC network for larger deformation measurement.Optics Express, 32(1):907–921, Jan 2024

    2024

  72. [80]

    Optical-numerical method based on a convolu- tional neural network for full-field subpixel displacement measurements.Optics Express, 29(6):9137–9156, Mar 2021

    Chaochen Ma, Qing Ren, and Jian Zhao. Optical-numerical method based on a convolu- tional neural network for full-field subpixel displacement measurements.Optics Express, 29(6):9137–9156, Mar 2021

    2021

  73. [81]

    Stereo-DICNet: An efficient and unified speckle match- ing network for stereo digital image correlation measurement.Optics and Lasers in Engi- neering, 179:108267, 2024

    Yahong Feng and Lianpo Wang. Stereo-DICNet: An efficient and unified speckle match- ing network for stereo digital image correlation measurement.Optics and Lasers in Engi- neering, 179:108267, 2024. 19

    2024

  74. [82]

    A tech- nique for in-situ displacement and strain measurement with laboratory-scale X-ray com- puted tomography.Experimental Techniques, 48(5):935–935, 2024

    OL Kafka, AK Landauer, JT Benzing, NH Moser, E Mansfield, and EJ Garboczi. A tech- nique for in-situ displacement and strain measurement with laboratory-scale X-ray com- puted tomography.Experimental Techniques, 48(5):935–935, 2024

    2024

  75. [83]

    Efficient and robust deformation mea- surement based on unsupervised learning.Measurement, 242:115908, 2025

    Jiashuai Yang, Yahong Feng, and Lianpo Wang. Efficient and robust deformation mea- surement based on unsupervised learning.Measurement, 242:115908, 2025

    2025

  76. [84]

    In situ analysis of plastic flow near interfaces and free surfaces.Measurement Science and Technology, 35(4):045601, 2024

    Deepika Gupta, Anirudh Udupa, and Koushik Viswanathan. In situ analysis of plastic flow near interfaces and free surfaces.Measurement Science and Technology, 35(4):045601, 2024

    2024

  77. [85]

    Stereo-DICNet2: A Unified and Physics-Guided Speckle Match- ing Network for Three-Dimensional Deformation Measurement.Experimental Mechanics, 66(2):399–415, 2026

    Y Feng and L Wang. Stereo-DICNet2: A Unified and Physics-Guided Speckle Match- ing Network for Three-Dimensional Deformation Measurement.Experimental Mechanics, 66(2):399–415, 2026

    2026

  78. [86]

    The optimal MLP-based model for displacement field measurement in 2D images and its application perspective

    Daria Mangileva. The optimal MLP-based model for displacement field measurement in 2D images and its application perspective. InCVIPPR ’24: Proceedings of the 2024 2nd Asia Conference on Computer Vision, Image Processing and Pattern Recognition, volume 36, pages 1–8, 2024

    2024

  79. [87]

    DICLab2D: An open-source digital image correlation algorithm for Julia language.SoftwareX, 33:102532, 2026

    Dennis Quaresma Pureza, José Luis Vital de Brito, Guilherme Santana Alencar, and Luís Augusto Conte Mendes Veloso. DICLab2D: An open-source digital image correlation algorithm for Julia language.SoftwareX, 33:102532, 2026

    2026

  80. [88]

    Digi- tal speckle photography in the presence of displacement gradients.Journal of the European Optical Society-Rapid Publications, 19:16, 2023

    León Schweickhardt, Andreas Tausendfreund, Dirk Stöbener, and Andreas Fischer. Digi- tal speckle photography in the presence of displacement gradients.Journal of the European Optical Society-Rapid Publications, 19:16, 2023

    2023

  81. [89]

    SUN-DIC: A Python-based open-source software tool for Digital Image Correlation.Advances in Engineering Software, 211:104043, 2025

    Gerhard Venter and Melody Neaves. SUN-DIC: A Python-based open-source software tool for Digital Image Correlation.Advances in Engineering Software, 211:104043, 2025

    2025

  82. [90]

    Interpretable DIC measurement neural network based on IC-GN algorithm framework.Optics Express, 34(6):9890–9905, 2026

    Lianpo Wang and Yantao Zhang. Interpretable DIC measurement neural network based on IC-GN algorithm framework.Optics Express, 34(6):9890–9905, 2026

    2026

  83. [91]

    Serial- Track: ScalE and Rotation Invariant Augmented Lagrangian particle tracking.SoftwareX, 19:101204, 2022

    Jin Yang, Yue Yin, Alexander K Landauer, Selda Buyukozturk, Jing Zhang, Luke Sum- mey, Alexander McGhee, Matt K Fu, John O Dabiri, and Christian Franck. Serial- Track: ScalE and Rotation Invariant Augmented Lagrangian particle tracking.SoftwareX, 19:101204, 2022

    2022

  84. [92]

    RAFTcorr: A deep learning digital image correlation framework with operating-boundary characterization

    Zixiang Tong, Lehu Bu, Qihang Shi, Runtian Du, and Jin Yang. RAFTcorr: A deep learning digital image correlation framework with operating-boundary characterization. 2026

    2026

  85. [93]

    Recent advances and applications of machine learning in experimental solid mechanics: A review.Applied Mechanics Reviews, 75(6):061001, 2023

    Hanxun Jin, Enrui Zhang, and Horacio D Espinosa. Recent advances and applications of machine learning in experimental solid mechanics: A review.Applied Mechanics Reviews, 75(6):061001, 2023

    2023

  86. [94]

    Digital image correlation for assessment of bridges’ technical state and remaining resource.Structural Control and Health Monitoring, 2024(1):1763285, 2024

    Nadiia Kopiika and Yaroslav Blikharskyy. Digital image correlation for assessment of bridges’ technical state and remaining resource.Structural Control and Health Monitoring, 2024(1):1763285, 2024

    2024

  87. [95]

    Digital image correlation technique for failure and crack propagation of fibre-reinforced polymer composites–A review.Composites and Advanced Materials, 33:26349833241253619, 2024

    Muhammad Irfan Naufal, King Jye Wong, Haris Ahmad Israr, Ali Farokhi Nejad, Seyed Saeid Rahimian Koloor, Khong Wui Gan, Mohd Khairul Faizi, and Geralt Siebert. Digital image correlation technique for failure and crack propagation of fibre-reinforced polymer composites–A review...

    2024

  88. [96]

    Free and open-source python-based software packages for digital im- age correlation in full-field displacement and strain measurements.Strain, 61(4):e70012, 2025

    Fasikaw Kibrete, Dereje Engida Woldemichael, Hailu Shimels Gebremedhen, and Temesgen Batu. Free and open-source python-based software packages for digital im- age correlation in full-field displacement and strain measurements.Strain, 61(4):e70012, 2025

    2025

  89. [97]

    VIC-2D Digital Image Correlation Software.https://www

    Correlated Solutions, Inc. VIC-2D Digital Image Correlation Software.https://www. correlatedsolutions.com/vic-2d/, 2024. Commercial software; accessed 2026-05-13

    2024

  90. [98]

    MatchID 2D Digital Image Correlation Software.https://www.matchid

    MatchID NV. MatchID 2D Digital Image Correlation Software.https://www.matchid. eu/, 2024. Commercial software; accessed 2026-05-13

    2024

  91. [99]

    ARAMIS 3D Optical Strain and Displacement Analysis.https://www

    ZEISS / GOM. ARAMIS 3D Optical Strain and Displacement Analysis.https://www. gom.com/en/products/zeiss-aramis, 2024. Commercial software; accessed 2026-05-13

    2024

  92. [100]

    DaVis Software.https://www.lavision.de/en/products/davis-software/

    LaVision. DaVis Software.https://www.lavision.de/en/products/davis-software/. Commercial software; accessed 2026-07-15

    2026

  93. [101]

    EikoSim Software.https://eikosim.com/

    EikoSim. EikoSim Software.https://eikosim.com/. Commercial software; accessed 2026-07-15

    2026

  94. [102]

    OpenCorr: An open source library for research and development of digi- tal image correlation.Optics and Lasers in Engineering, 165:107566, 2023

    Zhenyu Jiang. OpenCorr: An open source library for research and development of digi- tal image correlation.Optics and Lasers in Engineering, 165:107566, 2023

    2023

  95. [103]

    A q-factor-based digital image correlation algorithm (qDIC) for resolving finite deformations with degenerate speckle patterns.Experimental Mechanics, 58(5):815–830, 2018

    Alexander K Landauer, M Patel, DL Henann, and C Franck. A q-factor-based digital image correlation algorithm (qDIC) for resolving finite deformations with degenerate speckle patterns.Experimental Mechanics, 58(5):815–830, 2018

    2018

  96. [104]

    Numba: A LLVM-based Python JIT compiler

    Siu Kwan Lam, Antoine Pitrou, and Stanley Seibert. Numba: A LLVM-based Python JIT compiler. InProceedings of the Second Workshop on the LLVM Compiler Infrastructure in HPC, pages 1–6, 2015

    2015

  97. [105]

    Elizabeth M. C. Jones, Mark A. Iadicola, Jin Yang, Zixiang Tong, et al., editors.A Good Practices Guide for Digital Image Correlation. International Digital Image Correlation Soci- ety (iDICs), 2nd edition, 2025. 2nd edition; co-edited by authors of the present work

    2025

  98. [106]

    Unsupervised discovery of interpretable hyperelastic constitutive laws.Computer Methods in Applied Mechanics and Engineering, 381:113852, 2021

    Moritz Flaschel, Siddhant Kumar, and Laura De Lorenzis. Unsupervised discovery of interpretable hyperelastic constitutive laws.Computer Methods in Applied Mechanics and Engineering, 381:113852, 2021

    2021

  99. [107]

    Huaiqian You, Quinn Zhang, Colton J Ross, Chung-Hao Lee, Ming-Chen Hsu, and Yue Yu. A physics-guided neural operator learning approach to model biological tis- sues from digital image correlation measurements.Journal of Biomechanical Engineering, 144(12):121012, 2022

    2022

  100. [108]

    Combining unique planar biax- ial testing with full-field thickness and displacement measurement for spatial character- ization of soft tissues.Current Protocols, 2(7):e493, 2022

    Daniel Pearce, Mark Nemcek, and Colleen Witzenburg. Combining unique planar biax- ial testing with full-field thickness and displacement measurement for spatial character- ization of soft tissues.Current Protocols, 2(7):e493, 2022

    2022

  101. [109]

    Infer- ence of heterogeneous material properties via infinite-dimensional integrated dic.arXiv preprint arXiv:2408.10217, 2024

    Joseph Kirchhoff, Dingcheng Luo, Thomas O’Leary-Roseberry, and Omar Ghattas. Infer- ence of heterogeneous material properties via infinite-dimensional integrated dic.arXiv preprint arXiv:2408.10217, 2024

    2024 arXiv

  102. [110]

    Constitutive relations from images.Journal of Applied Mechanics, 92(8):081009, 2025

    Adeline Wihardja and Kaushik Bhattacharya. Constitutive relations from images.Journal of Applied Mechanics, 92(8):081009, 2025. 21

    2025

  103. [111]

    Sanjeev Kumar, D Thomas Seidl, Brian N Granzow, Jin Yang, and Jan Niklas Fuhg. A comparative study of calibration techniques for finite strain elastoplasticity: Numerically-exact sensitivities for femu and vfm.Computer Methods in Applied Mechan- ics and Engineering, 444:118159, 2025

    2025

  104. [112]

    Full-field calibration of coupled thermomechanical material models at finite strain.arXiv preprint arXiv:2606.05465, 2026

    L Spencer, William D Meador, Adrian Buganza Tepole, Brian N Granzow, Jin Yang, Manuel K Rausch, D Thomas Seidl, and Jan N Fuhg. Full-field calibration of coupled thermomechanical material models at finite strain.arXiv preprint arXiv:2606.05465, 2026. 22

    2026 arXiv

Pith tools

Reviewed August 1, 2026 · model on record in the stance chip above.