Search Results for author: Qizhi He

Found 9 papers, 1 papers with code

Neural-Integrated Meshfree (NIM) Method: A differentiable programming-based hybrid solver for computational mechanics

no code implementations21 Nov 2023 Honghui Du, Qizhi He

We present the neural-integrated meshfree (NIM) method, a differentiable programming-based hybrid meshfree approach within the field of computational mechanics.

Physics-informed machine learning Unity

A Multi-Resolution Physics-Informed Recurrent Neural Network: Formulation and Application to Musculoskeletal Systems

no code implementations26 May 2023 Karan Taneja, Xiaolong He, Qizhi He, J. S. Chen

This work presents a multi-resolution physics-informed recurrent neural network (MR PI-RNN), for simultaneous prediction of musculoskeletal (MSK) motion and parameter identification of the MSK systems.

Transfer Learning

A Hybrid Deep Neural Operator/Finite Element Method for Ice-Sheet Modeling

no code implementations26 Jan 2023 Qizhi He, Mauro Perego, Amanda A. Howard, George Em Karniadakis, Panos Stinis

One of the most challenging and consequential problems in climate modeling is to provide probabilistic projections of sea level rise.

Friction Uncertainty Quantification

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

no code implementations3 Sep 2022 Xiaolong He, Qizhi He, Jiun-Shyan Chen

In this study, the applicability of the proposed approach is demonstrated by modeling nonlinear biological tissues.

Physics-Informed Neural Network Method for Parabolic Differential Equations with Sharply Perturbed Initial Conditions

no code implementations18 Aug 2022 Yifei Zong, Qizhi He, Alexandre M. Tartakovsky

We propose a normalized form of ADE where the initial perturbation of the solution does not decrease in amplitude and demonstrate that this normalization significantly reduces the PINN approximation error.

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

no code implementations3 Mar 2022 Qizhi He, Yucheng Fu, Panos Stinis, Alexandre Tartakovsky

To improve the accuracy of voltage prediction at extreme ranges, we introduce a second (enhanced) DNN to mitigate the prediction errors carried from the 0D model itself and call the resulting approach enhanced PCDNN (ePCDNN).

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

no code implementations21 Jun 2021 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 (VRFB).

Patient Specific Biomechanics Are Clinically Significant In Accurate Computer Aided Surgical Image Guidance

no code implementations29 Jan 2020 Michael Barrow, Alice Chao, Qizhi He, Sonia Ramamoorthy, Claude Sirlin, Ryan Kastner

Augmented Reality is used in Image Guided surgery (AR IG) to fuse surgical landmarks from preoperative images into a video overlay.

Position

Physics-Informed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transport

1 code implementation6 Dec 2019 QiZhi He, David Brajas-Solano, Guzel Tartakovsky, Alexandre M. Tartakovsky

We apply this approach to assimilate conductivity, hydraulic head, and concentration measurements for joint inversion of the conductivity, hydraulic head, and concentration fields in a steady-state advection--dispersion problem.

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