Research

Research Overview:

Our group is broadly interested in computational mechanics and machine learning for applications related to energy, sustainability and resilience of the infrastructure and the built environment. In particular, we are interested in climate-resilient civil infrastructure, climate change mitigation technologies, and materials for energy and infrastructure sustainability. Some of the applications of our work include natural hazards (e.g., earthquakes and landslides), climate change adaptation, clean/renewable energy and decarbonization technologies (e.g., carbon sequestration, geothermal energy production), analysis and design of composite materials, multiscale material data fusion and advanced infrastructure materials and systems.

If you are interested in any of these topics or would like to discuss new ideas, check the information under Join us, and/or contact me via email.  

Research Areas:

Multi-scale multi-physics modeling of heterogeneous materials.

Our research includes characterization and modeling of heterogeneous materials (e.g. rocks, soils, cementitious materials, and composites) across scales, and development of multi-scale and multi-physics models to link the microstructure and macroscopic behavior of these materials. For this purpose, we combine experiments, mechanics, computational methods, and statistical and machine learning techniques.

Microstructure of synthetic and natural materials determine their overall properties and performance in engineering applications. We develop advanced techniques to incorporate microstructure of materials into computational models.

Machine-learning aided computational mechanics.

In this research area, we develop novel computational methods by enhancing computational mechanics tools with machine learning techniques. In particular, we are interested in developing fast and efficient computational techniques for modeling of complex systems.

Multi-scale material characterization and data fusion via statistical and machine-learning techniques. 

We combine advanced material characterization tools with statistical and machine learning techniques to probe the material structure and properties across scales to enhance analysis and design of materials.

Natural hazard assessment:

We leverage machine learning and computational mechanics tools to assess natural hazards and their impacts on civil infrastructure.

Renewable energy and decarbonization technologies:

We aim to address challenges in subsurface renewable energy technologies such as carbon sequestration and enhanced geothermal systems at both material level and system level. Examples include hydro-chemo-mechanical behavior of rocks, effects of rock-fluid interactions, and advanced computational techniques for large-scale modeling of subsurface systems.