Isogeometric analysis-based physics-informed graph neural network for studying traffic jam in neurons
From MaRDI portal
Publication:2679502
DOI10.1016/j.cma.2022.115757OpenAlexW4309265129MaRDI QIDQ2679502
Yongjie Jessica Zhang, Angran Li
Publication date: 20 January 2023
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.cma.2022.115757
isogeometric analysistraffic jamphysics-informed neural networkmicrotubule swirlneuron transport phenomena
Related Items (2)
Biomimetic IGA neuron growth modeling with neurite morphometric features and CNN-based prediction ⋮ MetaNO: how to transfer your knowledge on learning hidden physics
Uses Software
Cites Work
- Image-based geometric modeling and mesh generation
- Isogeometric shell analysis: the Reissner-Mindlin shell
- Variational multiscale residual-based turbulence modeling for large eddy simulation of incompressible flows
- Isogeometric analysis: CAD, finite elements, NURBS, exact geometry and mesh refinement
- Effect of vesicle traps on traffic jam formation in fast axonal transport
- A macroscopic model of traffic jams in axons
- A refinable space of smooth spline surfaces of arbitrary topological genus
- Probabilistic methods for a linear reaction-hyperbolic system with constant coefficients
- Truncated hierarchical tricubic \(C^0\) spline construction on unstructured hexahedral meshes for isogeometric analysis applications
- Machine learning in cardiovascular flows modeling: predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks
- Physics-informed graph neural Galerkin networks: a unified framework for solving PDE-governed forward and inverse problems
- NSFnets (Navier-Stokes flow nets): physics-informed neural networks for the incompressible Navier-Stokes equations
- PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain
- Seamless integration of design and Kirchhoff-Love shell analysis using analysis-suitable unstructured T-splines
- Physics-informed neural networks for high-speed flows
- Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
- Smooth cubic spline spaces on unstructured quadrilateral meshes with particular emphasis on extraordinary points: geometric design and isogeometric analysis considerations
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- An all-at-once approach for the optimal control of the unsteady Burgers equation
- Patient-specific vascular NURBS modeling for isogeometric analysis of blood flow
- Anatomically realistic lumen motion representation in patient-specific space-time isogeometric flow analysis of coronary arteries with time-dependent medical-image data
- Analysis-suitable unstructured T-splines: multiple extraordinary points per face
- Geometric Modeling and Mesh Generation from Scanned Images
- Isogeometric finite element data structures based on Bézier extraction of NURBS
- Isogeometric finite element data structures based on Bézier extraction of T-splines
- A Stochastic Compartmental Model for Fast Axonal Transport
- Approximate Traveling Waves in Linear Reaction-Hyperbolic Equations
- Alzheimer's disease: a mathematical model for onset and progression
- Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
This page was built for publication: Isogeometric analysis-based physics-informed graph neural network for studying traffic jam in neurons
