Radial basis function neural network (RBFNN) approximation of Cauchy inverse problems of the Laplace equation
DOI10.1016/j.camwa.2023.04.026OpenAlexW4367044590MaRDI QIDQ6103633
No author found.
Publication date: 5 June 2023
Published in: Computers \& Mathematics with Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.camwa.2023.04.026
Convex programming (90C25) Learning and adaptive systems in artificial intelligence (68T05) Inverse problems for PDEs (35R30) Finite difference methods for initial value and initial-boundary value problems involving PDEs (65M06) Numerical methods for inverse problems for boundary value problems involving PDEs (65N21) Numerical analysis (65-XX)
Related Items (1)
Cites Work
- A recurrent neural network for solving a class of generalized convex optimization problems
- On the uniqueness theorem of Holmgren
- Solution of stochastic nonlinear time fractional PDEs using polynomial chaos expansion combined with an exponential integrator
- An iterative boundary element method for Cauchy inverse problems
- On the limited memory BFGS method for large scale optimization
- Applied functional analysis. Functional analysis, Sobolev spaces and elliptic differential equations
- On simultaneous approximations by radial basis function neural networks
- A multiple-scale MQ-RBF for solving the inverse Cauchy problems in arbitrary plane domain
- Trefftz energy method for solving the Cauchy problem of the Laplace equation
- DGM: a deep learning algorithm for solving partial differential equations
- Optimal staggered-grid finite-difference method for wave modeling based on artificial neural networks
- Method of fundamental solutions for a Cauchy problem of the Laplace equation in a half-plane
- Fractional sliding mode based on RBF neural network observer: application to HIV infection mathematical model
- A stabilized radial basis-finite difference (RBF-FD) method with hybrid kernels
- SoftFEM: revisiting the spectral finite element approximation of second-order elliptic operators
- Artificial neural network approximations of Cauchy inverse problem for linear PDEs
- Localized MFS for the inverse Cauchy problems of two-dimensional Laplace and biharmonic equations
- A high order formula to approximate the Caputo fractional derivative
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- A new steepest descent differential inclusion-based method for solving general nonsmooth convex optimization problems
- Solution of the Cauchy problem using iterated Tikhonov regularization
- A dynamical regularization algorithm for solving inverse source problems of elliptic partial differential equations
- Convergence of a method based on the exponential integrator and Fourier spectral discretization for stiff stochastic PDEs
- fPINNs: Fractional Physics-Informed Neural Networks
- Convergence analysis for finite element approximation to an inverse Cauchy problem
- Nonlinear partial differential equations with applications
- Inverse problems for partial differential equations
- DeepBHCP: deep neural network algorithm for solving backward heat conduction problems
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
This page was built for publication: Radial basis function neural network (RBFNN) approximation of Cauchy inverse problems of the Laplace equation
