Research

Computational methods for advanced materials and manufacturing

In recent years, a novel opportunity has emerged within the realm of product, materials, and process (PMP) design, thanks to the advancements in advanced manufacturing, computational hardware, and artificial intelligence algorithms, all of which facilitate the processing and storage of vast amounts of big data. My research is centered on comprehending the intricate interplay between mechanics, materials, and manufacturing, with the ultimate objective of achieving the development of next-generation products. My interest lies in the development of computational tools and methodologies aimed at addressing the substantial challenges entailed in the manufacturing of advanced materials systems on a large scale.

Multiscale, multi-physics, and multifidelity modeling of intelligent materials system

3D/4D printing offers us the opportunity to print multifunctional materials. To fully leverage this potential, it is imperative to advance microstructure characterization across various scales, coupled with Multiphysics modeling to encompass the intricate processing involved. Moreover, the integration of experiments and simulations, characterized by differing fidelity levels, necessitates a robust strategy. The analysis of multifidelity data, in conjunction with calibration techniques for capturing uncertainties and sensor-generated noise, stands as a critical endeavor. Novel methodologies are paramount to surmount these challenges, ultimately enabling more proficient design and manufacturing of intelligent material systems. These advancements find application in diverse fields, including soft robotics and beyond.