A meshless solver for blood flow simulations in elastic vessels using a physics-informed neural network

Numerical optimization and variational techniques (65K10) Stokes and related (Oseen, etc.) flows (76D07) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20) Physiological flows (76Z05)

Cites Work

Extended ALE method for fluid-structure interaction problems with large structural displacements
Backpropagation and stochastic gradient descent method
Coupling Biot and Navier-Stokes equations for modelling fluid-poroelastic media interaction
Isogeometric fluid-structure interaction: Theory, algorithms, and computations
Cardiovascular flow simulation at extreme scale
A coupled momentum method for modeling blood flow in three-dimensional deformable arteries
A higher-order discontinuous Galerkin/arbitrary Lagrangian Eulerian partitioned approach to solving fluid-structure interaction problems with incompressible, viscous fluids and elastic structures
A coupled SPH-PD model for fluid-structure interaction in an irregular channel flow considering the structural failure
NSFnets (Navier-Stokes flow nets): physics-informed neural networks for the incompressible Navier-Stokes equations
Parallel physics-informed neural networks via domain decomposition
Adaptive activation functions accelerate convergence in deep and physics-informed neural networks
Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
A numerical method for solving the 3D unsteady incompressible Navier-Stokes equations in curvilinear domains with complex immersed boundaries
Scalable parallel methods for monolithic coupling in fluid-structure interaction with application to blood flow modeling
A projection semi-implicit scheme for the coupling of an elastic structure with an incompressible fluid
The immersed/fictitious element method for fluid-structure interaction: Volumetric consistency, compressibility and thin members
Gmsh: A 3-D finite element mesh generator with built-in pre- and post-processing facilities
Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations
Physics-Informed Neural Networks with Hard Constraints for Inverse Design
Modelling of fluid–structure interactions with the space–time finite elements: Arterial fluid mechanics
Splitting Methods Based on Algebraic Factorization for Fluid-Structure Interaction
Deep Neural Networks for Solving Large Linear Systems Arising from High-Dimensional Problems