## Overview

**The current research themes fall in the following categories:**

**Data-Driven Computational Solid Mechanics**for multiphase materials (e.g., engineered composites, biological tissues, geological materials), with a specific focus on modeling failure and other inelastic behaviors

**Multiscale and Multiphysics Simulation:**Applications include chemo-electro-mechanical skeletal muscle modeling, renewable energy storage system, flow and transport in porous media

**Machine Learning Enhanced Computational Methods**: Data assimilation, reduced-order modeling, and inverse problems

**AI for Science/Engineering**: Develop scientific machine learning approaches to understand multiphysics processes across scales, ranging from composite and energetic materials to climate systems

**Hybrid Computing****for****Advanced****Materials Design & Optimization**

## 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*

*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)