Dr. Xianglin Huang
Affiliated Researcher · Dongguk University–Seoul
Dr. Huang's research centres on data-driven methods for characterizing the dynamic mechanical behavior of engineering materials. His doctoral work at the University of Manchester (supervised by Prof. Q.M. Li) established a rigorous trilogy of methods — SVD/CP tensor decomposition combined with artificial neural networks — for verifying, determining, and representing dynamic flow stress equations from discrete SHPB experimental data. This work challenges and refines the foundations of the widely-used Johnson-Cook constitutive model.
As a postdoc at KAIST, he applies this expertise to lithium-ion battery impact safety: characterizing failure modes, developing deep learning (CNN-BiLSTM) frameworks for real-time acoustic emission-based damage monitoring, and building multiphysics FEM models of battery packs under dynamic loading. The long-term vision is to extend constitutive modeling methodology to battery electrode and separator materials — bridging mechanical and electrochemical safety frameworks.
Active Collaborations
Key research collaborators who have contributed to recent publications.
Prospective Students & Collaborators
I am always interested in working with motivated researchers at all levels — from undergraduate project students to postdoctoral researchers. Areas of particular interest for future collaborators include:
- Dynamic material characterization — SHPB testing, high-speed imaging, data-driven constitutive modeling
- Computational mechanics — ABAQUS, LS-DYNA, multiphysics FEM simulation, finite strain constitutive integration
- Battery safety — mechanical-electrochemical testing, acoustic emission monitoring, PHM algorithms
- Machine learning for mechanics — physics-informed neural networks, SVD/CP-based data decomposition, ANN surrogate models
If you are interested in joining or collaborating, please send an email to xianglin.huang@kaist.ac.kr with your CV and a brief statement of your research interests.
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