Materials Prognosis from Integrated Modeling & Experiment (M’) Lab
Research Scope
The M’ Lab researches emergent structural and material prognosis issues that involve the multiscale and stochastic nature of plasticity and fatigue cracking in structural materials. The research objective of the group is to leverage the ever-increasing capabilities in experimental observation and data analysis tools to provide new capabilities for prognosing reliability of advanced engineered structures and materials.Open Source Software
The M' Lab contributes to the open-source code, Bingo.- Bingo is an open source package for performing symbolic regression, though it can be used as a general purpose evolutionary optimization package. Go to the Bingo github page.
- CADSR is a novel deep symbolic regression approach to enhance the robustness and interpretability of data-driven mathematical expression discovery.Go to the CADSR arXiv.
Recent Publications
- (2025) Developing robust stress intensity factor models using Fourier-based data analysis to guide machine learning method selection and training
- (2025) Concurrent In-Situ High-Resolution Electron Backscatter Diffraction and Digital Image Correlation for Full-Field Stress-Strain
- (2025) Developing a continuum damage model for ceramic matrix composites using genetic programming based symbolic regression
- (2025) Full-field strain measurements on medical devices using digital image correlation: Considerations and practical examples
- (2025) Failure Mode Analysis of Microstructural Alignment in Freeze-Cast Scaffolds Using FEM
Good News
- Congratulations to Will Gilliland for passing his proposal defense titled, "Non-deterministic crystal plasticity parameter calibration from experimental material point stress-strain response"
- Congratulations to Will Jenkins for passing his proposal defense titled, "Leveraging physics-informed machine learning to develop material models for ceramic matrix composites"