Cornell potential: a neural network approach
From MaRDI portal
Publication:2414985
DOI10.1155/2019/3105373zbMath1412.81137arXiv1812.06802OpenAlexW3124789854MaRDI QIDQ2414985
Publication date: 20 May 2019
Published in: Advances in High Energy Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1812.06802
Neural networks for/in biological studies, artificial life and related topics (92B20) Closed and approximate solutions to the Schrödinger, Dirac, Klein-Gordon and other equations of quantum mechanics (81Q05) Computational methods for problems pertaining to quantum theory (81-08)
Related Items (3)
Quark-antiquark effective potential in symplectic quantum mechanics ⋮ Fractional effective quark-antiquark interaction in symplectic quantum mechanics ⋮ Mass spectrum of mesons via the WKB approximation method
Cites Work
- Unnamed Item
- Equivalence of logarithmic perturbation theory and expansion of the superpotential in supersymmetric quantum mechanics
- Detecting a trend change in cross-border epidemic transmission
- An introduction to neural network methods for differential equations
- Scalar-vector-pseudoscalar Cornell potential for a spin-\({1/2}\) particle under spin and pseudospin symmetries: \({1+1}\) dimensions
- Precise numerical solutions of potential problems using the Crank-Nicolson method
- Analytic solution of the Schrödinger equation for the Coulomb-plus-linear potential. I. The wave functions
- Universal approximation bounds for superpositions of a sigmoidal function
- Equivalence of two alternative approaches to Schrödinger equations
- Numerical solution of the Schrödinger equation by neural network and genetic algorithm
This page was built for publication: Cornell potential: a neural network approach
