Wiera Bielajewa | Searcharxiv

arXiv2026

PYVALE: A Fast, Scalable, Open-Source 2D Digital Image Correlation (DIC) Engine Capable of Handling Gigapixel Images

Background: Digital Image Correlation (DIC) is a widely used full-field measurement technique, but both open-source and commercial packages often have limitations such as operating-system restrictions, lack of support for deployment on computing clusters, and poor scalability to gigapixel-scale images common in Scanning Electron Microscopy DIC (SEM-DIC). Objective: Pyvale is an open-source software package designed for sensor simulation, uncertainty quantification, placement optimization, and calibration/validation. A key component of this is the development of a dedicated 2D DIC module intended for standalone use and integration within broader workflows. Methods: Pyvale provides a user-friendly Python interface with performant compiled routines underneath. At its core is a multithreaded, reliability-guided DIC algorithm. Its open-source MIT license enables wide deployment, including on computing clusters and in automated pipelines. Results: Benchmarking with the publicly available 2D DIC challenge 2.0 dataset shows that Pyvale achieves metrological performance comparable to existing commercial and open-source DIC codes. It can correlate gigapixel-scale image pairs in under 5 minutes on high-specification desktop workstations, with memory peaking at approximately 50 GB. Conclusions: Pyvale's strong metrological foundation, coupled with its scalability for SEM-DIC, positions it as a platform for sustained, community-driven development. Its design and licensing provide a foundation for future improvements in open-source DIC and integration into experimental design and validation workflows.

eess.IV↗

arXiv2024

A novel, finite-element-based framework for sparse data solution reconstruction and multiple choices

Digital twinning offers a capability of effective real-time monitoring and control, which are vital for cost-intensive experimental facilities, particularly the ones where extreme conditions exist. Sparse experimental measurements collected by various diagnostic sensors are usually the only source of information available during the course of a physical experiment. Consequently, in order to enable monitoring and control of the experiment (digital twinning), the ability to perform inverse analysis, facilitating the full field solution reconstruction from the sparse experimental data in real time, is crucial. This paper shows for the first time that it is possible to directly solve inverse problems, such as solution reconstruction, where some or all boundary conditions (BCs) are unknown, by purely using a finite-element (FE) approach, without needing to employ any traditional inverse analysis techniques or any machine learning models, as is normally done in the field. This novel and efficient FE-based inverse analysis framework employs a conventional FE discretisation, splits the loading vector into two parts corresponding to the known and unknown BCs, and then defines a loss function based on that split. In spite of the loading vector split, the loss function preserves the element connectivity. This function is minimised using a gradient-based optimisation. Furthermore, this paper presents a novel modification of this approach, which allows it to generate a range of different solutions satisfying given requirements in a controlled manner. Controlled multiple solution generation in the context of inverse problems and their intrinsic ill-posedness is a novel notion, which has not been explored before. This is done in order to potentially introduce the capability of semi-autonomous system control with intermittent human intervention to the workflow.

cs.CE↗