Neural networks are a methodological basis of materials genome

Main Article Content

Victor S Abrukov*
Weiqiang Pang
Darya A Anufrieva

Abstract

Materials Genome is an analytical and calculation tool that: contains all relationships between all variables of the object; allows to calculate of the values of one part of variables through others; allows to solve of direct and inverse problems; allows to predict of the characteristics of objects, which have not been investigated experimentally yet; allows to predict technology parameters to obtain an object with desired characteristics as well as allows to execute virtual experiment for conditions which cannot be organized or difficultly to organize. The paper presents the Neural Networks as a methodological Materials Genome basis. Possible areas of Neural Networks use are the development of new materials and their production.

Downloads

Download data is not yet available.

Article Details

Abrukov, V. S., Pang, W., & Anufrieva, D. A. (2023). Neural networks are a methodological basis of materials genome. Trends in Computer Science and Information Technology, 8(1), 012–015. https://doi.org/10.17352/tcsit.000063
Short Communications

Copyright (c) 2023 Abrukov VS, et al.

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Kolmogorov A. On the representation of continuous functions of several variables by superpositions of continuous functions of a smaller number of variables. Izvestiya AN SSSR. 1956; 108:179-182.

Arnold V. On the function of three variables. Izvestiya AN SSSR. 1957; 114(4):679-681.

Gorban AN. Generalized approximation theorem and computational capabilities of neural networks. Siberian Journal of Computational Mathematics. 1998; 1(1):11-24.

Hecht-Nielsen R. Kolmogorov's Mapping Neural Network Existence Theorem. IEEE First Annual Int. Conf. on Neural Networks, San Diego. 1987; 3:11-13.

Abrukov VS, Karlovich EV, Afanasyev VN, Semenov YV, Abrukov SV. Creation of propellant combustion models by means of data mining tools. International Journal of Energetic Materials and Chemical Propulsion. 2010; 9(5):385-394.

Chandrasekaran N, Oommen C, Kumar VRS, Lukin AN, Abrukov VS, Anufrieva DA. Prediction of Detonation Velocity and N − O Composition of High Energy C − H − N − O Explosives by Means of Artificial Neural Networks. J. Propellants, Explosives, Pyrotechnics. 2019; 44(5):579-587.

Abrukov VS, Lukin AN, Oommen C, Chandrasekaran N, Bharath RS, Kumar VRS, Anufrieva DA. Development of the Multifactorial Computational Models of the Solid Propellants Combustion by Means of Data Science Methods - Phase II. Technology and Investment, Proceedings of a meeting held 9-11 July 2018, Cincinnati, Ohio, USA. Held at the AIAA Propulsion and Energy Forum. 2018; AIAA 2018-4961.

Werbos PJ. Beyond regression: New tools for prediction and analysis in the behavioral sciences (Doctoral dissertation). Harvard University, Cambridge, MA. 1974.

Galushkin AI. Synthesis of multilayer image recognition systems. M.: Energy. 1974.

Rumelhart DE, Hinton GE, Williams RJ. Learning Internal Representations by Error Propagation. In: Parallel Distributed Processing. Cambridge. 1986; 1:318-362.

Abrukov VS, Kochergin AV, Anufrieva DA. Artificial neural networks as a means of generalizing experimental data. Bulletin of the Chuvash University. 2016; 3:155-162. https://www.researchgate.net/publication/333211155_Iskusstvennye_nejronnye_seti_kak_sredstvo_obobsenia_eksperimentalnyh_dannyh_artificial_neural_networalks_as_fa_meata.

Anufrieva DA, Koshcheev MI, Abrukov VS. Application of data mining methods in physics research. Multifactor detonation models. In the collection: High-speed hydrodynamics and shipbuilding. Collection of scientific papers of the XII International Summer Scientific School-Conference dedicated to the 155th anniversary of the birth of Academician A.N. Krylov. 2018; 221-226. https://www.researchgate.net/publication/333210244_Primenenie_metodov_intellektualnogo_analiza_dannyh_v_fiziceskih_issledovaniah_mnogofaktornye_modeli_detonacii .

Anufrieva DA, Abrukov VS, Lukin AN, Oommen C, Sanalkumar VR, Chandrasekaran N. Generalized multifactor computational models of the detonation of condensed and gas systems. 2019. https://www.researchgate.net/publication/334126580_Generalized_multifactor_computational_models_of_the_detonation_of_condensed_systems

Abrukov VS, Anufrieva DA, Sanalkumar VR, Mariappan A. Multifactor Computational Models of the Effect of Catalysts on the Combustion of Ballistic Powders (experimental results of Denisyuk team) Direct Tasks, Virtual Experiments and Inverse Problems. 2020. https://www.researchgate.net/publication/344727996_Multifactor_Computational_Models_of_the_Effect_of_Catalysts_on_the_Combustion_of_Ballistic_Powders_experimental_results_of_Denisyuk_team_Direct_Tasks_Virtual_Experiments_and_Inverse_Problems

Abrukov VS, Anufrieva DA, Sanalkumar VR, Mariappan A. Comprehensive study of AP particle size and concentration effects on the burning rate of composite AP / HTPB propellants by means of neural networks: Development of the multifactor computational models. Direct tasks and inverse problems & virtual experiments. 2020; 1-20. https://doi.org/10.13140/RG.2.2.19019.62242

Abrukov V, Anufrieva DA, Lukin AN, Oommen C, Sanalkumar VR, Chandrasekaran N. Effects of metals and termites adds on combustion of double-based solid propellants: Development of the multifactor computational models of the solid propellants combustion by means of data science methods. Virtual experiments and propellant combustion genome. 2019. https://www.researchgate.net/publication/334172872_Effects_of_metals_and_termites_adds_on_combustion_of_double-_based_solid_propellants_development_of_the_multifactor_computational_models_of_the_solid_propellants_combustion_by_means_of_data_science_me

Pang W, Abrukov V, Anufrieva D, Chen D. Burning rate prediction of solid rocket propellant (SRP) with high-energy materials genome (HEMG). Crystals. 2023; 13(5):237. doi: 10.3390/cryst13050237.