My research focuses on achieving mechanistic understanding of the structure-properties relations for complex heterogeneous materials by exploiting synergistic combinations of computational, experimental and machine learning methods.
A key theme is the development of efficient computational models for the prediction of the strength and fracture properties based on experimental data describing the size, shape and location of the defects/heterogeneities at the micro-scale. Applications range from additive manufactured metals to cast materials and composites.
Fracture mechanics, Physics-informed neural networks, Digital volume correlation, X-ray tomography
Metal 3D printing, architected materials and composites
Computational: Finite element analysis (FEA), computational homogenization, physics-informed neural networks, machine learning for prediction of material behavior
Experimental: Digital image correlation (DIC), digital volume correlation (DVC), synchrotron X-ray microdiffraction
Composite Materials Engineering
