Neural network approach for the calculation of potential coefficients in quantum mechanics
DOI10.1016/j.cpc.2017.01.006zbMath1376.81025OpenAlexW2577681817MaRDI QIDQ1685803
Patricio Cumsille, Camilo Reyes, Carlos M. Reyes, Sebastian Ossandon
Publication date: 20 December 2017
Published in: Computer Physics Communications (Search for Journal in Brave)
Full work available at URL: http://hdl.handle.net/10533/227880
finite element methodinverse problemsradial basis functionartificial neural networkcoefficients of the potential functioneigenvalues of the Schrödinger operator
Inverse problems for PDEs (35R30) Selfadjoint operator theory in quantum theory, including spectral analysis (81Q10) Neural nets and related approaches to inference from stochastic processes (62M45)
Cites Work
- Unnamed Item
- Unnamed Item
- Neural network solution for an inverse problem associated with the Dirichlet eigenvalues of the anisotropic Laplace operator
- Numerical solution of the nonlinear Schrödinger equation by feedforward neural networks
- A finite-difference method for the numerical solution of the Schrödinger equation
- An efficient Chebyshev-Lanczos method for obtaining eigensolutions of the Schrödinger equation on a grid
- Finite element approximation of eigenvalue problems
- Eigenvalue Approximation by Mixed and Hybrid Methods
- Mixed and Hybrid Finite Element Methods
- An accurate method for numerical calculations in quantum mechanics
- Mixed Finite Element Methods and Applications
This page was built for publication: Neural network approach for the calculation of potential coefficients in quantum mechanics