Physics-Informed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transport. - QiZhi He; David Barajas-Solano; Guzel Tartakovsky; Alexandre M. Tartakovsky (CoRR, 2019)

Physics-constrained deep neural network method for estimating parameters in a redox flow battery. - QiZhi He; Panos Stinis; Alexandre M. Tartakovsky (CoRR, 2021)

Physics-informed machine learning with conditional Karhunen-Loève expansions. - Alexandre M. Tartakovsky; David A. Barajas-Solano; QiZhi He (J. Comput. Phys., 2021)

Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery. - QiZhi He; Yucheng Fu; Panos Stinis; Alexandre M. Tartakovsky (CoRR, 2022)

Physics-Informed Neural Network Method for Parabolic Differential Equations with Sharply Perturbed Initial Conditions. - Yifei Zong; QiZhi He; Alexandre M. Tartakovsky (CoRR, 2022)

Neural-Integrated Meshfree (NIM) Method: A differentiable programming-based hybrid solver for computational mechanics. - Honghui Du; QiZhi He (CoRR, 2023)

A hybrid deep neural operator/finite element method for ice-sheet modeling. - QiZhi He; Mauro Perego; Amanda A. Howard; George Em Karniadakis; Panos Stinis (J. Comput. Phys., 2023)

History-Aware Neural Operator: Robust Data-Driven Constitutive Modeling of Path-Dependent Materials. - Binyao Guo; Zihan Lin; QiZhi He (CoRR, 2025)

Differentiable neural-integrated meshfree method for forward and inverse modeling of finite strain hyperelasticity. - Honghui Du; Binyao Guo; QiZhi He (Eng. Comput., 2025)

JAX-MPM: A Learning-Augmented Differentiable Meshfree Framework for GPU-Accelerated Lagrangian Simulation and Geophysical Inverse Modeling. - Honghui Du; QiZhi He (CoRR, 2025)