Variable separated physics-informed neural networks based on adaptive weighted loss functions for blood flow model

Artificial neural networks and deep learning (68T07) Learning and adaptive systems in artificial intelligence (68T05) Navier-Stokes equations for incompressible viscous fluids (76D05) Physiological flows (76Z05) Physiological flow (92C35)

Cites Work

Unnamed Item
Unnamed Item
Unnamed Item
A fully implicit domain decomposition based ALE framework for three-dimensional fluid-structure interaction with application in blood flow computation
Well-balanced high-order numerical schemes for one-dimensional blood flow in vessels with varying mechanical properties
Multi-scale computational model of three-dimensional hemodynamics within a deformable full-body arterial network
Hemelb: A high performance parallel lattice-Boltzmann code for large scale fluid flow in complex geometries
Stable loosely-coupled-type algorithm for fluid-structure interaction in blood flow
On the limited memory BFGS method for large scale optimization
A three-dimensional computational method for blood flow in the heart. I: Immersed elastic fibers in a viscous incompressible fluid
Numerical analysis of blood flow in the heart
One-dimensional modelling of a vascular network in space-time variables
Neural network modeling for near wall turbulent flow.
A variational data assimilation procedure for the incompressible Navier-Stokes equations in hemodynamics
Machine learning in cardiovascular flows modeling: predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks
A generic physics-informed neural network-based constitutive model for soft biological tissues
Non-invasive inference of thrombus material properties with physics-informed neural networks
Optimally weighted loss functions for solving PDEs with neural networks
NSFnets (Navier-Stokes flow nets): physics-informed neural networks for the incompressible Navier-Stokes equations
When and why PINNs fail to train: a neural tangent kernel perspective
Physics-informed neural networks for high-speed flows
Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Adaptive deep neural networks methods for high-dimensional partial differential equations
Time difference physics-informed neural network for fractional water wave models
Physics-informed neural networks based on adaptive weighted loss functions for Hamilton-Jacobi equations
Lagrangian wall shear stress structures and near-wall transport in high-Schmidt-number aneurysmal flows
Exact fully 3D Navier–Stokes solutions for benchmarking
Computational modelling of 1D blood flow with variable mechanical properties and its application to the simulation of wave propagation in the human arterial system
Flow in Collapsible Tubes with Discontinuous Mechanical Properties: Mathematical Model and Exact Solutions
Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks
A ‘well‐balanced’ finite volume scheme for blood flow simulation
Solving high-dimensional partial differential equations using deep learning
Adaptive Deep Learning for High-Dimensional Hamilton--Jacobi--Bellman Equations
A Direct Eulerian GRP Scheme for a Blood Flow Model in Arteries
Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
Deep learning in fluid dynamics
On the coupling of 3D and 1D Navier-Stokes equations for flow problems in compliant vessels

This page was built for publication: Variable separated physics-informed neural networks based on adaptive weighted loss functions for blood flow model