Research

Physical Intelligence

“Enable materials to think, learn and adapt”

Research Topic

Design the Materials

We apply our computational methods to achieve both forward modeling and inverse design of functional materials, particularly robotic materials, and their manufacturing process. Besides the classical process-structure-property link, we also explore the impact of composition, external stimuli, and aleatoric uncertainty.

 

  1. Wang, L., Yerramilli, S., Iyer, A., Apley, D., Zhu, P. and Chen, W., 2022. Scalable gaussian processes for data-driven design using big data with categorical factors. Journal of Mechanical Design, 144(2), p.021703.
  2. Wang, L. and Chen, W., 2024. Mixed-Variable Global Sensitivity Analysis for Knowledge Discovery and Efficient Combinatorial Materials Design. Journal of Mechanical Design, 146, pp.051706-1.
Research Topic

Design with Materials

We use AI/ML and optimization to create scalable framework for the concurrent design of materials, structures, and stimuli in physical intelligence systems. This includes metamaterials, programmable materials systems and soft robots, allowing us to achieve specific responsive functionalities like mechanical cloaking, shape morphing, and wave guiding.

  1. Wang, L., Boddapati, J., Liu, K., Zhu, P., Daraio, C. and Chen, W., 2022. Mechanical cloak via data-driven aperiodic metamaterial design. Proceedings of the National Academy of Sciences, 119(13), p.e2122185119.
  2. Wang, L., Chang, Y., Wu, S., Zhao, R.R. and Chen, W., 2023. Physics-aware differentiable design of magnetically actuated kirigami for shape morphing. Nature Communications, 14(1), p.8516.
  3. Wang, L., Chan, Y.C., Ahmed, F., Liu, Z., Zhu, P. and Chen, W., 2020. Deep generative modeling for mechanistic-based learning and design of metamaterial systems. Computer Methods in Applied Mechanics and Engineering, 372, p.113377.
Research Topic

Design for Nature- & Human-Centered Applications

We are collaborating with interdisciplinary experts to utilize the enhanced physical intelligence for applications in medical treatment, ecology, and mechanical computing. We are also interested in exploring how artificial physical intelligence can offer insights into natural phenomena, such as biological processes and evolution.