Data-driven mechanics

We are organizing a EUROMECH colloquium on data-driven mechanics and physics of materials, to be held on May 21-23, 2025, in Gothenburg. Topics of interest include (but are not limited to):

- Traditional and physics-enhanced machine learning for surrogate modeling
- Learning and exploiting latent representations of materials behavior
- Image-based machine learning methods
- Data-driven process modeling of materials
- Generative learning for material optimization
- Micromechanics-based data-driven methods for designing materials

A one-year post-doc position is available in our research group at the University of Houston with a start date of 1 September 2021. 

We are interested in understanding how microstructural aspects govern material failure. Our approach relies on formulating theoretical frameworks that are informed by experiments and implementing them computationally.

Join us to expand in more novel directions:

(i) Data-driven plasticity and failure;

(ii) Phase-field approaches to plasticity and failure;