APPLICATION OF DIFFERENTIAL EQUATIONS IN ARTIFICIAL INTELLIGENCE

Authors

  • Jurayev Shaxzod Shuxratjonvich Asia International University

Keywords:

Artificial Intelligence, Differential Equations, Neural ODEs, Optimization, Robotics, Machine Learning, Dynamic Modeling

Abstract

This paper explores the crucial role of differential equations in the field of artificial intelligence (AI), particularly in modeling, analysis, and optimization of intelligent systems. Differential equations provide a rigorous mathematical foundation for describing dynamic processes, learning mechanisms, and adaptive behaviors in AI models. Their integration into AI contributes to the development of algorithms capable of predicting, controlling, and optimizing complex systems. Special emphasis is placed on their applications in neural networks, natural language processing, robotics, and machine learning optimization. The paper also discusses the advantages, challenges, and future directions of combining AI with differential equations, highlighting their potential impact on autonomous systems, healthcare, and large-scale data processing.

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References

Boyce, W. E., & DiPrima, R. C. (2017). Elementary Differential Equations and Boundary Value Problems. Wiley.

Khalil, H. K. (2002). Nonlinear Systems (3rd Edition). Prentice Hall.

Kloeden, P. E., & Platen, E. (2011). Numerical Solution of Stochastic Differential Equations. Springer.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). “Deep learning.” Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539

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Published

2025-09-17

How to Cite

Jurayev Shaxzod Shuxratjonvich. (2025). APPLICATION OF DIFFERENTIAL EQUATIONS IN ARTIFICIAL INTELLIGENCE. Journal of Applied Science and Social Science, 15(09), 306–309. Retrieved from https://www.internationaljournal.co.in/index.php/jasass/article/view/1775

Issue

Vol. 15 No. 09 (2025): Volume 15 Issue 09

Section

Articles

License

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