We aim to develop next-generation high-fidelity simulation frameworks that integrate advanced data assimilation, digital twin, and machine learning capabilities for complex natural and engineered systems under extreme conditions, including damage and failure. Our current research spans the following core themes:
- Intelligent Scientific Computing: Advancing novel computational methods for Software 2.0 that supports high-performance modeling and simulation with data-driven capacities.
- Multiscale mechanics: Multi-physics phenomena in heterogenous porous media and composite (ceramics, cementitious materials, geomaterials, hard and soft tissues, etc.) through integrating model reduction, multiscale modeling and physics-informed machine learning.
- Data-Driven Modeling for Natural and Engineered System Resilience: Advance the knowledge of scientific machine learning and reduced-order modeling for multiscale and multiphysics simulation and inverse modeling related to material failure, natural hazards, earth systems, and material design.
Research Topics:
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: Du et al. Computers and Geotechnics (2023); 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)
Modeling density-driven flow for CO2 sequestration
Ref: Du et al. (2023) Modeling density-driven flow in porous media by physics-informed neural networks for CO2 sequestration. Computers and Geotechnics.
FEM-SUPG
Enriched PINN with Domain Decompostion
Machine learning enhanced meshfree modeling, multiscale modeling, reduced-order modeling
Hyper reduced-order RKPM modeling for thermal fatigue in Electronic Devices
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)
Geohazard modeling
Differentiable Simulation
Collapse of granular column (GPU-based simulation with >2 million particles for Drucker–Prager material)
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)