MUMBAI, India, May 1 -- Intellectual Property India has published a patent application (202641049819 A) filed by Madhankumar C; Dr. K. S. Araththi; Dr. S. Saranyasri; Dr. C. Ramkumar; and K. Priya, Pollachi, Tamil Nadu, on April 19, for 'mathematical modelling and optimization techniques for intelligent engineering systems.'
Inventor(s) include Dr. K. S. Araththi; Dr. S. Saranyasri; Dr. C. Ramkumar; and K. Priya.
The application for the patent was published on May 1, under issue no. 18/2026.
According to the abstract released by the Intellectual Property India: "Mathematical Modelling and Optimization Techniques for Intelligent Engineering Systems Abstract Mathematical modelling and optimization techniques play a pivotal role in the design and development of intelligent engineering systems by enabling efficient decision-making, system adaptability, and performance enhancement. This study presents a comprehensive framework that integrates advanced mathematical models with optimization strategies to address complex, real-world engineering challenges. The proposed approach utilizes a combination of deterministic and stochastic modelling techniques to represent system dynamics, constraints, and uncertainties accurately. Furthermore, the research incorporates modern optimization methods, including metaheuristic algorithms, convex optimization, and hybrid intelligent techniques, to achieve optimal resource allocation, system stability, and operational efficiency. The integration of artificial intelligence and machine learning methods enhances predictive capabilities and supports real-time optimization in dynamic environments. The developed models are validated across multiple engineering domains, such as energy systems, communication networks, and smart manufacturing, demonstrating improved convergence speed, reduced computational complexity, and enhanced solution accuracy. The results indicate that the proposed framework significantly outperforms conventional optimization approaches in terms of scalability, robustness, and adaptability. This work contributes to the advancement of intelligent engineering systems by providing a unified modelling and optimization paradigm capable of addressing multi-objective, nonlinear, and high-dimensional problems. The proposed methodology is highly applicable to next-generation smart systems, enabling sustainable and efficient engineering solutions."
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