116 citations · 225 across the 7 of their papers we have counts for
7 papers
Differentiable Neural-Integrated Meshfree Method for Forward and Inverse Modeling of Finite Strain Hyperelasticity
Honghui Du, Binyao Guo, QiZhi He
The present study aims to extend the novel physics-informed machine learning approach, specifically the neural-integrated meshfree (NIM) method, to model finite-strain problems cha…
A Multi-Resolution Physics-Informed Recurrent Neural Network: Formulation and Application to Musculoskeletal Systems
Karan Taneja, Xiaolong He, Qizhi He +1
This work presents a multi-resolution physics-informed recurrent neural network (MR PI-RNN), for simultaneous prediction of musculoskeletal (MSK) motion and parameter identificatio…
A Hybrid Deep Neural Operator/Finite Element Method for Ice-Sheet Modeling
QiZhi He, Mauro Perego, Amanda A. Howard +2
One of the most challenging and consequential problems in climate modeling is to provide probabilistic projections of sea level rise. A large part of the uncertainty of sea level p…
Physics-informed neural networks of the Saint-Venant equations for downscaling a large-scale river model
Dongyu Feng, Zeli Tan, QiZhi He
Large-scale river models are being refined over coastal regions to improve the scientific understanding of coastal processes, hazards and responses to climate change. However, coar…
Deep autoencoders for physics-constrained data-driven nonlinear materials modeling
Xiaolong He, Qizhi He, Jiun-Shyan Chen
Physics-constrained data-driven computing is an emerging computational paradigm that allows simulation of complex materials directly based on material database and bypass the class…
Physics-Informed Neural Network Method for Parabolic Differential Equations with Sharply Perturbed Initial Conditions
Yifei Zong, QiZhi He, Alexandre M. Tartakovsky
In this paper, we develop a physics-informed neural network (PINN) model for parabolic problems with a sharply perturbed initial condition. As an example of a parabolic problem, we…
Physics-constrained deep neural network method for estimating parameters in a redox flow battery
QiZhi He, Panos Stinis, Alexandre Tartakovsky
In this paper, we present a physics-constrained deep neural network (PCDNN) method for parameter estimation in the zero-dimensional (0D) model of the vanadium redox flow battery (V…