Integrating Machine Learning Into Simulation Processes
DOI:
https://doi.org/10.29284/p7rrpr81Keywords:
Machine Learning, Simulation, Data-driven modeling, Hybrid systems, Real-time adaptation.Abstract
In a simulation process, the size and level of details at which a physical, biological, social, or computational system is being modeled or simulated, is hug. Machine learning based approaches are adopted to handle simulation complexity, ML based approaches are indispensable for the majority of applications in real life. The ML simulation merges rule-based and data-driven models in the attempt to furnish systems that are more adaptable, efficient, and precise. It is a hybrid model that gives more power to hard-coded parameter-based simulation models as these models learn and adapt from data in real-time. This paper considers machine learning-based simulation methods in the spheres of methods, application, problems, and future research. Simulation using ML is the shift of thinking from deterministic modeling to learning-type adaptive systems that can best accommodate uncertainty, complexity, and dynamic conditions more so than traditional models.
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
