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Abstract The term Artificial Inteligence refers to systems or devices that simulate human intelligence to perform tasks that can improve themselves based on the information they collect. Artificial intelligence manifests itself in a number of forms. Artificial intelligence is more about the ability to think and analyze data than it is about a specific shape or function. Neural networks are a model of intelligence models and are known as (Nodes, Neurons) to watch them with the property of experience, experimental information, to make them, by adjusting the weights. It is analogous to the human brain in an industry that gains knowledge by training and stores knowledge using forces within neurons called synaptic weights. There is also a dynamic ring, which gives biologists the opportunity to draw on these networks to understand the evolution of biological phenomena. The present work deals with a theoretical study of some parameters in solid state and in p-p collisions at high energy in the framework of Artificial intelligence (AI) which contains the model of Artificial Neural Network (ANN) for simulation these parameters and also prediction the values of experiments that have not been made yet with describing them by mathematical function obtained from obtained this model. The first chapter : This chapter deals with a concept the Artificial intelligence , Philosophy of AI , Goals of AI , What Contributes to AI? , Programming without and with AI , Applications of AI. In the second chapter: We discussed the study of one model from Artificial Intelligence (AI) which is the model of the Artificial Neural Network for studying some photovoltaic characteristics of NiPc/p-Si and some characteristics of p-p interactions .This chapter explains philosophy of AI , goals and techniques of AI , classification and task domain of AI, difference between Human and Machine Intelligence, AI Contributions , applications of AI. In the third chapter: Neural networks were designed for studying the model the photovoltaic properties of Nickel–phthalocyanine (NiPc/p-Si) heterojunction. Eight neural networks were trained to simulate the experimental data their constructions were introduced in this chapter. The ANN predicted results at values of 298 K for dark current–voltage characteristics and 373 K for variation of log I with log Vof NiPc/p-Si heterojunction which measured experimentally introduce an excellent accordance with the experimental data. Also, the predicted ANN results at 273 and 340 K for dark current–voltage characteristics and at 273 ,360 and 380 K for variation x of log I with log V of NiPc/p-Si heterojunction which are not measured previously present logical results. The most important result presented is the mathematical relation which describes the behavior of the photovoltaic properties of NiPc/p-Si (organic/inorganic) heterojunction. In the fourth chapter: Neural networks were designed for studying some parameters of p-p interactions at high energy ( Inelastic p-p cross section, Particle pseudo rapidity density, Transverse momentum distribution, Multiplicity dependence of identified hadron production in pp, Invariant-mass distributions of Weaklydecaying Strange Hadrons, Pseudo-Rapidity Distribution Differential yields of particle/antiparticle production yields). Twenty neural networks were trained to simulate the experimental data their constructions were introduced in this chapter. 1.Inelastic p-p cross section Inelastic p-p cross section as a function of c.m. energy in the range √s ≈10 GeV–500 TeV from p-¯p (UA5 , E710 and CDF) and p-p (ALICE ATLAS,CMS, TOTEM ) colliders, as well as the AUGER result at √s = 57 TeV derived from cosmic-ray data. Neural Network is used to simulate these experimental data for inelastic p-p cross section as a function of energy √s and compared to the prediction of pythia. 2. Particle pseudo rapidity density Neural Network was designed for studying Particle pseudo rapidity density for p-p interactions. The c.m. energy evolution of the charged hadron pseudo rapidity density at = 0 in the range √s = 10 GeV–800 TeV is inelastic data which measured at Sp¯pS (UA5 )and LHC (ALICE, ATLAS and CMS) colliders. Neural Network is used to simulate these experimental data for evolution of the charged particle pseudo rapidity density at midrapidity, , as a function of collision energy, √s, for inelastic p-p collisions and compared to the prediction of pythia. 3. Transverse momentum distribution Neural Network was designed for studying transverse momentum distribution for( xi p-p), (p-¯p )interactions Evolution of at midrapidity as a function of c.m. energy √s. Data points show existing collider results. Neural Network is used to simulate these experimental data for Evolution of at midrapidity as a function of c.m. energy √s and compared to the prediction of pythia. 4. Multiplicity dependence of identified hadron production in pp Neural Network was designed for studying Multiplicity dependence of charged and neutral kaon yields obtained using mid-pseudo rapidity charged particle multiplicities (| |< 0.5) and the charged particle multiplicities within the pseudo rapidity. Data collected by ALICE in the LHC pp run of the year 2010 are used. Neural Network is used to simulate these experimental data for multiplicity dependence of charged and neutral kaon yields obtained using mid-pseudo rapidity charged particle multiplicities (|ƞ |< 0.5). 5. Invariant-mass distributions of Weakly-decaying Strange Hadrons Neural Network was designed for studying Invariant-mass distributions of , Λ, , k and kk decay candidates in selected ranges for multiplicity in pp collisions at √ s = 7 TeV. Data collected by ALICE in the LHC pp run of the year 2010 are used. Neural Network is used to simulate these experimental data for Invariant-mass distributions of , Λ, , k and kk decay candidates in selected ranges. 6. Pseudo-Rapidity Distribution Neural Network was designed for studying Pseudo-Rapidity Distribution for p-p interactions at √ s=2.36, the ISR(√ s =23.6 GeV ) to the Tevatron (CDF data, √ s =1.8 TeV). Increasing the energy results in an increase in multiplicity[, the Large Hadron Collider (LHC) √s = 7 TeV and (√ s =8 TeV recorded by the ATLAS detector at the LHC). Neural Network is used to simulate these experimental data for the pseudorapidity distributions (d /dɳ ) of the created particles in p-p interactions at different energies √s(23.6 ,53 ,200 ,900 ,1800. 2360,7000 ,8000 GeV)and predicted known (2.36 TeV) and unkown energies (10 and 14 TeV) for the pseudo-rapidity distributions (dn/dɳ ) of the created particles in p-p interactions at energies √s. xii 7. Differential yields of particle/antiparticle production yields Neural Network was designed for studing Differential yields of particle/antiparticle production yields .The results are shown for a selection of event classes , indicated by roman numbers in brackets ,with decreasing multiplicity. The data have been performed ALICE detector at LHC -differential yields of ̅ ̅ measured in |y|<0.5 for a selection of event classes with progressively decreasing< /d > . Neural Network is used to simulate these experimental for - differential yields of ̅ ̅ measured in |y|<0.5. * The most important results obtained by applying the artificial neural network model to simulate practical values related to some physical parameters related to solid state physics and high energy physics: 1- A general mathematical equation describing the physical behavior of the subject of study in the fields of solid state physics and high energy physics. 2-Using the artificial neural network model, high-precision simulation results were obtained compared to the practical values of the physical parameters in the two fields (solid state physics - high energies physics). Also, the accuracy of the high results was proven when compared to the results obtained. 3- Using the artificial neural network model, process values included in the model inputs were predicted (not practically measured) and high-precision results were obtained. Summarizing the above, the study demonstrated the high efficiency of the artificial neural network model in simulating and predicting different physical parameters related to solid state physics and high energy physics, as well as its ability to give a general equation describing the physical behavior in the two cases. |