Extreme learning machine collocation for the numerical solution of elliptic PDEs with sharp gradients
DOI10.1016/j.cma.2021.114188OpenAlexW3202131078WikidataQ114196875 ScholiaQ114196875MaRDI QIDQ2246423
Gianluca Fabiani, Constantinos I. Siettos, Francesco Calabrò
Publication date: 16 November 2021
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2012.05871
collocation methodsboundary layerartificial neural networkspartial differential equationsextreme learning machinesigmoidal transfer functions
Artificial neural networks and deep learning (68T07) Spectral, collocation and related methods for initial value and initial-boundary value problems involving PDEs (65M70)
Related Items (15)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Approximation by neural networks with scattered data
- Isogeometric collocation for elastostatics and explicit dynamics
- Approximation results for neural network operators activated by sigmoidal functions
- Trends in extreme learning machines: a review
- A collection of 2D elliptic problems for testing adaptive grid refinement algorithms
- A posteriori estimators for the \(h\)-\(p\) version of the finite element method in 1D
- A fully automatic \(hp\)-adaptivity
- Multilayer feedforward networks are universal approximators
- An approximation by neural networks with a fixed weight
- Nonlinearity creates linear independence
- DGM: a deep learning algorithm for solving partial differential equations
- DPM: a deep learning PDE augmentation method with application to large-eddy simulation
- A derivative-free method for solving elliptic partial differential equations with deep neural networks
- On the approximation by single hidden layer feedforward neural networks with fixed weights
- Conservative physics-informed neural networks on discrete domains for conservation laws: applications to forward and inverse problems
- Neural-net-induced Gaussian process regression for function approximation and PDE solution
- Negative results for approximation using single layer and multilayer feedforward neural networks
- Null rules for the detection of lower regularity of functions
- Machine learning for semi linear PDEs
- An accurate FIC-FEM formulation for the 1D advection-diffusion-reaction equation
- Solving high-dimensional partial differential equations using deep learning
- DeepXDE: A Deep Learning Library for Solving Differential Equations
- Approximation by superpositions of a sigmoidal function
This page was built for publication: Extreme learning machine collocation for the numerical solution of elliptic PDEs with sharp gradients