## 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.)

## 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)