A robust radial point interpolation method empowered with neural network solvers (RPIM-NNS) for nonlinear solid mechanics
DOI10.1016/j.cma.2024.117159MaRDI QIDQ6588318
Yizheng Wang, Jinshuai Bai, G. R. Liu, T. Rabczuk, Y. T. Gu, Xiqiao Feng
Publication date: 15 August 2024
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
neural networkradial basis functionradial point interpolation methodnonlinear computational mechanics
Spectral, collocation and related methods for initial value and initial-boundary value problems involving PDEs (65M70) Numerical and other methods in solid mechanics (74S99)
Cites Work
- Title not available (Why is that?)
- Title not available (Why is that?)
- Torsional instability of stretched rubber cylinders
- Elastic instabilities in rubber
- An incremental approach to the solution of snapping and buckling problems
- Error estimates for interpolation by compactly supported radial basis functions of minimal degree
- A new meshless local Petrov-Galerkin (MLPG) approach in computational mechanics
- Modeling low Reynolds number incompressible flows using SPH
- On the optimal shape parameters of radial basis functions used for 2-D meshless methods
- Compactly supported radial basis functions for shallow water equations.
- A data-driven smoothed particle hydrodynamics method for fluids
- The neural particle method - an updated Lagrangian physics informed neural network for computational fluid dynamics
- A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
- Deep autoencoder based energy method for the bending, vibration, and buckling analysis of Kirchhoff plates with transfer learning
- CENN: conservative energy method based on neural networks with subdomains for solving variational problems involving heterogeneous and complex geometries
- A general neural particle method for hydrodynamics modeling
- A novel stabilized node-based smoothed radial point interpolation method (SNS-RPIM) for coupling analysis of magneto-electro-elastic structures in hygrothermal environment
- An improved cell-based smoothed radial point interpolation method using condensed shape functions for 3D interior acoustic problems
- A deep energy method for finite deformation hyperelasticity
- An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications
- A new family of constitutive artificial neural networks towards automated model discovery
- An arc-length method including line searches and accelerations
- Element‐free Galerkin methods
- Smoothed Particle Hydrodynamics
- A point interpolation meshless method based on radial basis functions
- Historical Development of the Newton–Raphson Method
- A radial basis function method for global optimization
- Deep learning phase‐field model for brittle fractures
- BINN: a deep learning approach for computational mechanics problems based on boundary integral equations
- Physics-informed radial basis network (PIRBN): a local approximating neural network for solving nonlinear partial differential equations
- Data-driven nonparametric identification of material behavior based on physics-informed neural network with full-field data
- An introduction to programming physics-informed neural network-based computational solid mechanics
This page was built for publication: A robust radial point interpolation method empowered with neural network solvers (RPIM-NNS) for nonlinear solid mechanics
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6588318)