Qizhi He

Assistant Professor

University of Minnesota

United States

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Qizhi He is an Assistant Professor at the University of Minnesota, USA. His research focuses on advanced computational methods and machine learning applications in engineering, particularly in the modeling of path-dependent materials, hyperelasticity, and energy systems. He has published extensively on topics such as physics-informed neural networks and their applications in various fields, including CO2 sequestration and energy storage.

Articles (16)

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2025

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2021

Collaborators (1)

Alexandre M. Tartakovsky

UNITED STATES

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Qizhi He

Research Interests

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Articles (16)

History-aware neural operator: Robust data-driven constitutive modeling of path-dependent materials

Differentiable neural-integrated meshfree method for forward and inverse modeling of finite strain hyperelasticity

Neural-Integrated Meshfree (NIM) Method

Improved training of physics-informed neural networks for parabolic differential equations with sharply perturbed initial conditions

Modeling density-driven flow in porous media by physics-informed neural networks for CO2 sequestration

Physics‐Informed Neural Networks of the Saint‐Venant Equations for Downscaling a Large‐Scale River Model

A hybrid deep neural operator/finite element method for ice-sheet modeling

A multi-resolution physics-informed recurrent neural network: formulation and application to musculoskeletal systems

A coupled reinforcement learning and IDAES process modeling framework for automated conceptual design of energy and chemical systems

Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery

Physics-constrained deep neural network method for estimating parameters in a redox flow battery

A Feature-Encoded Physics-Informed Parameter Identification Neural Network for Musculoskeletal Systems

Deep autoencoders for physics-constrained data-driven nonlinear materials modeling

Physics‐Informed Neural Network Method for Forward and Backward Advection‐Dispersion Equations

Manifold learning based data-driven modeling for soft biological tissues

Physics-informed machine learning with conditional Karhunen-Loève expansions

Collaborators (1)

Alexandre M. Tartakovsky

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