Search Results for author:
Found 10 papers, 2 papers with code
Differentiable Neural-Integrated Meshfree Method for Forward and Inverse Modeling of Finite Strain Hyperelasticity
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 characterized by nonlinear elasticity and large deformations.
Neural-Integrated Meshfree (NIM) Method: A differentiable programming-based hybrid solver for computational mechanics
We present the neural-integrated meshfree (NIM) method, a differentiable programming-based hybrid meshfree approach within the field of computational mechanics.
A Multi-Resolution Physics-Informed Recurrent Neural Network: Formulation and Application to Musculoskeletal Systems
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.
A Hybrid Deep Neural Operator/Finite Element Method for Ice-Sheet Modeling
One of the most challenging and consequential problems in climate modeling is to provide probabilistic projections of sea level rise.
Deep autoencoders for physics-constrained data-driven nonlinear materials modeling
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
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
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
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
Augmented Reality is used in Image Guided surgery (AR IG) to fuse surgical landmarks from preoperative images into a video overlay.
Physics-Informed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transport
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.
