A Review Article on Relation between Mathematical Modelling and Machine Learning
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摘要
A mathematical model is an abstract description of a concrete system using mathematical concepts and language. The process of developing a mathematical model is termed mathematical modelling. Mathematical models are used in applied mathematics and in the natural sciences such as physics, biology, earth science, chemistry and engineering disciplines like computer science, electrical engineering as well as in non-physical systems such as the social sciences. It can be also taught as a subject in its own right. The use of mathematical models to solve problems in business or military operations is a large part of the field of operations research. ML is one of today’s most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in ML has been driven both by the development of new learning algorithms and theory and by the on-going explosion in the availability of online data and low-cost computation. It is a collection of a variety of algorithms like neural networks, case-based reasoning, genetic programming, decision trees, random forests, self-organizing maps, support vector machines, etc. Because of the ML-based approaches' modelling capabilities, science and engineering have made substantial use of them.. In this paper the main key is importance of ML in mathematical modelling in health issues like Diagnosis recognition of disease, Covid-19 etc. Further we present the scope of mathematical modelling with ML in many different areas which is helpful for researchers.
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