Overview
The current research interests fall in the following categories:
(1) Data-Driven Computing in solid mechanics involving inelasticity and material failure
(2) Meshfree Methods for modeling porous and composite materials
(3) Machine Learning Enhanced Computational Methods: Reduced-order modeling, PDE-constrained optimization, and inverse problems.
(4) AI for Science/Engineering: Develop scientific machine learning approaches to understand multi-scale and multi-physics processes related to geosystems (e.g., subsurface transport, climate models, and geo-mechanics) and energy storage systems
(5) Advanced Manufacturing and Materials Design using hybrid computational approaches (AI, topology optimization, multiscale modeling, etc.)
Research highlights. (a) MS: Thermomechanical analysis of lap-joint of Al alloys under laser welding process. (b) MS: Multiscale simulation of skeletal muscle tissues based on image pixel-based meshfree computational framework. (c) MS & EBO: Topological design of compliance structures (e.g. multi-physics actuators). (d) MS & ML: Reduced-order modeling for fatigue life prediction of viscoplastic solder joints in electronic packages based on hyper-reduced meshfree methods. (e) EBO & ML: Geomaterial properties estimation and hydraulic state prediction by physics-informed machine learning for the subsurface system of Hanford site (high-level nuclear waste-contaminated area). (f) MS & ML: Physics-constrained data-driven modeling of soft biological tissues.
Data-driven engineering and mechanics
Develop novel computational methods under an optimization framework that integrates scientific computing, data-driven machine learning, and particle-based methods to the modeling of soft tissues, fracture mechanics, and nonlinear solids.
References: He, X., He, Q. & Chen, J.S., Comput Methods Appl Mech Eng (2021); He, X., He, Q. & Chen, J.S., Data-Centric Engineering (2020); He, Q. et al., Journal of Biomechanics (2020); He, Q. & Chen, J.S., Comput Methods Appl Mech Eng (2019)
Physics-informed/Scientific machine learning
Develop effective and robust deep learning methods based on physics-informed neural networks to solve multiphysics problems arising in engineering science.
References: He, Q. & Tartakovsky, A., Water Resources Research (2021); Tartakovsky, A., Barajas-Solano, D., He, Q., Journal of Computational Physics (2020); He, Q. et al. Advances in Water Resources (2020)
Machine learning enhanced meshfree modeling, multiscale modeling, reduced-order modeling
1) Meshfree reduced-order modeling of fracture mechanics & nonlinear solid
References: 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)
2) Multiscale modeling of advanced materials and manufacturing
References: Zhang, Y. et al. Int J Numer Method Biomed Eng (2020); He, Q. et al. Int J Adv Manuf Technol (2018); Wei, H., He, Q. et al. Journal of Laser Applications (2017)
Deep learning and computational methods for material design
Reference: He, Q. et al. Computational Mechanics (2014); Wang Y. et al. Computers & Structures (2013, 2014)