Dazhi Zhao 赵大志
Hi! I am Dazhi Zhao (赵大志), a third-year undergraduate student majoring in Engineering Mechanics at the School of Aerospace Engineering and Applied Mechanics, Tongji University.
I am currently doing my visiting in the CMSL Group at HKUST, working with Prof. Tianju Xue.
My research interests include: Data-driven design Differentiable simulation Inverse problems
At Tongji University, I am advised by Prof. Keke Tang from the FCM Lab and Prof. Rui Fan from the MIAS Group.
News
2026/06/13
Our phase field paper on an open-source Julia framework was accepted by AES.
2026/04/18
Our Composite Structures paper, Physics-constrained neural networks for high-fidelity composite failure envelopes, is now online.
2026/03/17
Our preprint on physics-guided diffusion models for inverse design of disordered metamaterials is now available on arXiv.
2026/03/08
Our Thin-Walled Structures paper on autoregressive inverse design of disordered metamaterials was accepted.
2026/01/28
Started my internship at HKUST!
2025/07/18
Presented our work on rapid prediction and impact-parameter identification of interlaminar damage at ICDM 2025 in Singapore.
Selected Publications
Autoregressive Inverse Design of Disordered Metamaterials for Target Nonlinear Response
Zhao, D., Xiang, Y., Zhang, P., Liu, N., Wang, X., and Tang, K. (2026). "Autoregressive inverse design of disordered metamaterials for target nonlinear response." Thin-Walled Structures, 225, 114793.
Physics-Constrained Neural Networks for High-Fidelity Composite Failure Envelopes
Zhang, R., Zhao, D., Zhang, P., and Tang, K. (2026). "Physics-constrained neural networks for high-fidelity composite failure envelopes." Composite Structures, 120358.
Physics-Guided Diffusion Models for Inverse Design of Disordered Metamaterials
Xie, Z., Xu, W., Zhao, D., Zhang, W., Dong, D., Xu, B., Liu, N., Mao, S., and Xue, T. (2026). "Physics-guided diffusion models for inverse design of disordered metamaterials." arXiv preprint arXiv:2603.16209.
Projects
GUI for Phase-Field Fracture Workflows
An interface-oriented research tool that streamlines phase-field fracture simulation workflows, making model setup, execution, and result inspection more accessible.
Reinforcement Learning for Torque Control in a Four-Link Mechanism
A control-oriented project that explores how reinforcement learning can generate effective torque strategies for a coupled four-link mechanical system.