Multifunctional Materials Design Across Scales

We develop machine learning-enhanced modeling and design tools for multifunctional and multiphysics systems, with a focus on reduced-order modeling and accelerating simulation, analysis, and optimization across scales.

Our current efforts include:

1. Thermomechanical and failure modeling of electronic devices

2. Topology optimization of multifunctional materials

3. Integration of generative AI with mesoscale modeling to accelerate the discovery and design of high-performance polymer materials.

Hyper reduced-order RKPM modeling for thermal fatigue in Electronic Devices

Relevant Papers: 

• Kaneko, S., Wei, H., He, Q., et al., Journal of the Mechanics and Physics of Solids (2021)
• He, Q., Chen, J. S., & Marodon, C., Computational Mechanics (2019)

Topology optimization of nonlinear materials (Meshfree methods)

Relevant Papers:

• He, Wang, Kang, Comput Mech (2014)
• Wang, Kang, He, Comput Struct (2014)