Han Feng Zhai

I work in Mechanics and Computation (academic tree). My doctoral research focuses on multiscale materials modeling (i.e., defects → microstructures → properties), such as metal strain hardening and homogenization of digital rocks. I had some industrial experiences at Mitsubishi Electric Research Labs (AI & energy systems), and Tokyo Electron (computational modeling of 3DI).

I am interested in solving problems in mechanics of materials from atomistic to continuum scales. My research interests are computational mechanics, materials theory, scientific machine learning, and inverse optimization for materials modeling and design, with potential impact on energy, biotechnology, and additive manufacturing. In particular:

• Theoretical mechanics of solids, e.g., theoretical model for dislocations, mechanistic model for MPEA.
• Faster and data-driven compute, e.g., graph neural surrogate for polycrystal plasticity, neural ODE for HVAC.
• Materials design and manufacturing, e.g., optimizations for crystal design, digital design of antimicrobial materials.

I enjoyed serving the community and teaching applied mathematics and solid mechanics. I will be co-organizing CompFest 2027 and currently a TA for ME123: Computational Engineering. Previously, I TA'd Mechanics: Elasticity and Inelasticity (see short intro, evaluations) and Finite Element Analysis (see short intro, evaluations). See teaching notes for problem sessions and course reviews.