This chapter highlights the shift from traditional mathematical modeling, grounded in theoretical principles and differential equations, to data-driven approaches that leverage machine learning and empirical data. While conventional models offer structured frameworks for understanding systems, they can be limited in flexibility and scalability. In contrast, data-driven models uncover patterns from large datasets and handle complex, non-linear systems without relying solely on theoretical assumptions. By integrating machine learning with traditional models, accuracy and adaptability improve significantly. Different machine learning techniques, including supervised and reinforcement learning, extract valuable insights, especially in cases where traditional models falter. Hybrid models combining physics-based approaches with data-driven techniques enhance prediction capabilities, such as in energy consumption forecasts for smart grids. The chapter also addresses challenges like data quality and model transparency, emphasizing how hybrid models improve interpretability and predictive power. Case studies demonstrate the benefits of integrating machine learning with traditional models in enhancing model robustness and accuracy. In summary, the fusion of machine learning and traditional methods creates more reliable models, especially for complex systems where conventional approaches face limitations. [1, 2, 3, 4, 5].
Author
(s) Details
Ghada
Awad Elkarim Mohammed Ahmed Ahmed
Department of Mathematics, Faculty of Science, Al-Baha University,
Alaqiq 65799, Saudi Arabia.
Please see the book here:- https://doi.org/10.9734/bpi/mcscd/v7/2649
No comments:
Post a Comment