Title :
A Neural Network (NN) Approach to Solving a Static-non-exchange Scattering of Electron-Hydrogen
Author :
Bin Shahrir, Mohammad Shazri ; Ratnavely, Kurunathan
Author_Institution :
R&D, Inst. Sains Mat. (ISM), Univ. Malaya Telekom Malaysia, Kuala Lumpur, Malaysia
Abstract :
In this present work is to numerically estimate via neural network the scattering elastic-collision phase shift from electron hydrogen interaction. Previous works have shown reliable results using runge-kutta 4th order (RK-4). This can be achieved by solving the 2nd Order Differential Equation (ODE) that is found commonly in physical scattering problem. A number of trial functions was tested that describe the Schrodinger Equation in which solves the static field approximation of the wave equation. Results have shown comparable but inferior results relatively to the RK-4 method. It can be said that NN approach shows promise with the advantage of continuous estimation but lack the accuracy that can be produced by RK-4.
Keywords :
Schrodinger equation; approximation theory; atom-electron collisions; hydrogen neutral atoms; neural nets; physics computing; H; Schrodinger equation; continuous estimation; electron hydrogen interaction; neural network approach; numerical estimation; physical scattering problem; scattering elastic-collision phase shift; second order differential equation; static field approximation; static-nonexchange scattering; trial functions; wave equation; Artificial neural networks; Biological neural networks; Differential equations; Equations; Quantum mechanics; Scattering; hydrogen; neural network; quantum; runge kutta; scattering;
Conference_Titel :
Computational Intelligence, Modelling and Simulation (CIMSim), 2013 Fifth International Conference on
Conference_Location :
Seoul
Print_ISBN :
978-1-4799-2308-3
DOI :
10.1109/CIMSim.2013.11