scholarly journals Cornell Potential: A Neural Network Approach

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Halil Mutuk
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Schrödinger Equation ◽  
Schrodinger Equation ◽  
Linear Potential ◽  
Network Approach ◽  
Neural Network Approach ◽  
Cornell Potential ◽  
Potential Parameters ◽  
Good Agreement

We solved Schrödinger equation with Cornell potential (Coulomb-plus-linear potential) by using neural network approach. Four different cases of Cornell potential for different potential parameters were used without a physical relevance. Besides that charmonium, bottomonium and bottom-charmed spin-averaged spectra were also calculated. Obtained results are in good agreement with the reference studies and available experimental data.

A Neural Network Approach for Solution of the Schrödinger Equation for a Particle in the Pöschl-Teller Potential

2021 ◽  
Author(s):  
Garvit Arora ◽  
Apoorva Singh ◽  
Ashutosh Vishwa Bandhu
Keyword(s):  
Neural Network ◽  
Schrödinger Equation ◽  
Schrodinger Equation ◽  
State Energy ◽  
Neural Network Approach ◽  
Analytical Results ◽  
The Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Good Agreement

We use a shallow artificial neural network (ANN) to solve the Schrödinger equation and find the ground state energy and wavefunction for a particle in the Pöschl-Teller potential. We also use the finite difference method (FDM) to find these quantities and compare the results obtained using the two methods with the analytical results. Results obtained using the neural network are in good agreement with the analytical results. Our work reaffirms the promising nature of application of artificial neural networks in quantum mechanics.

A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuzzy Neural Network ◽  
Treatment Time ◽  
Calculation Model ◽  
Average Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural ◽  
Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

A RBF Neural Network Approach for Fitting Creep Curve of Sandstone

2010 ◽  
Vol 171-172 ◽  
pp. 274-277
Author(s):  
Yun Liang Tan ◽  
Ze Zhang
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Creep Curve ◽  
Bp Neural Network ◽  
Rbf Neural Network ◽  
Reference Value ◽  
Network Approach ◽  
Neural Network Approach ◽  
Training Time ◽  
Set Up

In order to quest an effective approach for predicate the rheologic deformation of sandstone based on some experimental data, an improved approaching model of RBF neural network was set up. The results show, the training time of improved RBF neural network is only about 10 percent of that of the BP neural network; the improved RBF neural network has a high predicating accuracy, the average relative predication error is only 7.9%. It has a reference value for the similar rock mechanics problem.

A neural network approach for the prediction of the refractive index based on experimental data

2011 ◽  
Vol 47 (2) ◽  
pp. 883-891 ◽  
Author(s):  
Alex Alexandridis ◽  
Eva Chondrodima ◽  
Konstantinos Moutzouris ◽  
Dimos Triantis
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Refractive Index ◽  
Network Approach ◽  
Neural Network Approach

scholarly journals ALGEBRAIC APPROACH TO THE SPECTRAL PROBLEM FOR THE SCHRÖDINGER EQUATION WITH POWER POTENTIALS

2000 ◽  
Vol 15 (02) ◽  
pp. 209-226 ◽  
Author(s):  
R. N. FAUSTOV ◽  
V. O. GALKIN ◽  
A. V. TATARINTSEV ◽  
A. S. VSHIVTSEV
Keyword(s):  
Schrödinger Equation ◽  
Schrodinger Equation ◽  
Mass Spectra ◽  
Infinite System ◽  
Algebraic Approach ◽  
Heavy Quarkonium ◽  
Algebraic Equations ◽  
Cornell Potential ◽  
Relativistic Equations ◽  
Good Agreement

The method reducing the solution of the Schrödinger equation for several types of power potentials to the solution of the eigenvalue problem for the infinite system of algebraic equations is developed. The finite truncation of this system provides high accuracy results for low-lying levels. The proposed approach is appropriate both for analytic calculations and for numerical computations. This method allows also to determine the spectrum of the Schrödinger-like relativistic equations. The heavy quarkonium (charmonium and bottomonium) mass spectra for the Cornell potential and the sum of the Coulomb and oscillator potentials are calculated. The results are in good agreement with experimental data.

scholarly journals An artificial neural network approach to recognise kinetic models from experimental data

2020 ◽  
Vol 135 ◽  
pp. 106759 ◽  
Author(s):  
Marco Quaglio ◽  
Louise Roberts ◽  
Mohd Safarizal Bin Jaapar ◽  
Eric S. Fraga ◽  
Vivek Dua ◽  
...  
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Kinetic Models ◽  
Network Approach ◽  
Neural Network Approach ◽  
Artificial Neural ◽  
Artificial Neural Network Approach
Sign in / Sign up

Export Citation Format

Share Document