4 citations · 4 across the 3 of their papers we have counts for
6 papers
Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery
QiZhi He, Yucheng Fu, Panos Stinis +1
Numerical modeling and simulation have become indispensable tools for advancing a comprehensive understanding of the underlying mechanisms and cost-effective process optimization a…
Physics-Informed Neural Network Method for Forward and Backward Advection-Dispersion Equations
QiZhi He, Alexandre M. Tartakovsky
We propose a discretization-free approach based on the physics-informed neural network (PINN) method for solving coupled advection-dispersion and Darcy flow equations with space-de…
Patient Specific Biomechanics Are Clinically Significant In Accurate Computer Aided Surgical Image Guidance
Michael Barrow, Alice Chao, Qizhi He +3
Augmented Reality is used in Image Guided surgery (AR IG) to fuse surgical landmarks from preoperative images into a video overlay. Physical simulation is essential to maintaining…
Physics-Informed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transport
QiZhi He, David Brajas-Solano, Guzel Tartakovsky +1
Data assimilation for parameter and state estimation in subsurface transport problems remains a significant challenge due to the sparsity of measurements, the heterogeneity of poro…
Physics-Informed Machine Learning with Conditional Karhunen-Loève Expansions
Alexandre M. Tartakovsky, David A. Barajas-Solano, Qizhi He
We present a new physics-informed machine learning approach for the inversion of PDE models with heterogeneous parameters. In our approach, the space-dependent partially-observed p…
A Physics-Constrained Data-Driven Approach Based on Locally Convex Reconstruction for Noisy Database
Qizhi He, Jiun-Shyan Chen
Physics-constrained data-driven computing is an emerging hybrid approach that integrates universal physical laws with data-driven models of experimental data for scientific computi…