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A Review on Real-Time Mathematical Modelling and Simulation of Engineering Systems Using Advanced Data Analytics Techniques

Paper ID:
IJSDIP_2026_VOLUME 14_ISSUE 2_35-39
Author's Name:
Dr. Jayesh Dhuri, Ms. Kavita Chavan, Ms. Seema Barhate, Dr. Ganesh Mhatre, Mr. Suhas Waghmare
Volume:
14
Issue:
Issue 2
Year:
2026
Pages:
35-39

Abstract

Real-time mathematical modelling has become an essential component in modern engineering systems, enabling efficient monitoring, prediction, and control of dynamic processes. Traditional modelling approaches based on physical laws often struggle to capture non-linearities and uncertainties present in real-world systems. With the rapid advancement of data analytics, machine learning, and artificial intelligence, new hybrid modelling approaches have emerged that combine physical models with data-driven techniques. This review paper provides a comprehensive analysis of classical modelling techniques, data-driven approaches, and hybrid frameworks for real-time applications. It further examines recent advancements between 2020 and 2025, identifies key challenges, and outlines future research directions. The findings indicate that hybrid models offer superior performance in terms of accuracy, adaptability, and computational efficiency.

Keywords

Real-time Modelling, Machine Learning, Hybrid Systems, Data Analytics, Engineering Systems, LSTM, IoT

References

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