An adaptive discrete physics-informed neural network method for solving the Cahn-Hilliard equation
DOI10.1016/J.ENGANABOUND.2023.06.031zbMATH Open1537.65151MaRDI QIDQ6539904
Huiqing Zhu, Jian He, Xinxiang Li
Publication date: 15 May 2024
Published in: Engineering Analysis with Boundary Elements (Search for Journal in Brave)
Could not fetch data.
Artificial neural networks and deep learning (68T07) Finite element, Rayleigh-Ritz and Galerkin methods for initial value and initial-boundary value problems involving PDEs (65M60) Multistep, Runge-Kutta and extrapolation methods for ordinary differential equations (65L06) Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65M99) PDEs in connection with mechanics of deformable solids (35Q74)
Cites Work
- Title not available (Why is that?)
- Title not available (Why is that?)
- Title not available (Why is that?)
- A numerical method based on the boundary integral equation and dual reciprocity methods for one-dimensional Cahn-Hilliard equation
- Computationally efficient solution to the Cahn-Hilliard equation: Adaptive implicit time schemes, mesh sensitivity analysis and the 3D isoperimetric problem
- The least squares spectral element method for the Cahn-Hilliard equation
- On the limited memory BFGS method for large scale optimization
- A generalized diffusion model for growth and dispersal in a population
- Applications of semi-implicit Fourier-spectral method to phase field equations
- The numerical solution of linear ordinary differential equations by feedforward neural networks
- The numerical solution of Cahn-Hilliard (CH) equation in one, two and three-dimensions via globally radial basis functions (GRBFs) and RBFs-differential quadrature (RBFs-DQ) methods
- Simulation of the phase field Cahn-Hilliard and tumor growth models via a numerical scheme: element-free Galerkin method
- A novel sequential method to train physics informed neural networks for Allen Cahn and Cahn Hilliard equations
- Improving the accuracy and consistency of the scalar auxiliary variable (SAV) method with relaxation
- DeLISA: deep learning based iteration scheme approximation for solving PDEs
- A revisit of the energy quadratization method with a relaxation technique
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- A numerical method for the Cahn-Hilliard equation with a variable mobility
- A multigrid finite element solver for the Cahn-Hilliard equation
- Discovering phase field models from image data with the pseudo-spectral physics informed neural networks
- Front migration in the nonlinear Cahn-Hilliard equation
- Large Sample Properties of Simulations Using Latin Hypercube Sampling
- Stability and convergence of a second-order mixed finite element method for the Cahn–Hilliard equation
- DeepXDE: A Deep Learning Library for Solving Differential Equations
- Solving Allen-Cahn and Cahn-Hilliard Equations using the Adaptive Physics Informed Neural Networks
- Free Energy of a Nonuniform System. I. Interfacial Free Energy
- A discontinuous Galerkin method for the Cahn-Hilliard equation
- A stable and conservative finite difference scheme for the Cahn-Hilliard equation
This page was built for publication: An adaptive discrete physics-informed neural network method for solving the Cahn-Hilliard equation
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6539904)
