Machine Learning Design Thinking for Fluid Models. Issue 1 (June 2021)

• Record Type: Journal Article
• Authors: Priyadharshini, P.; Divya, P.
• Is Part Of: Journal of physics. Volume 1947:Issue 1(2021)
• Publication Date: 2021-06
• Subjects: Machine Learning -- Neural Networks -- Artificial intelligence -- Fluid flow models -- Modeling Techniques; Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1947/1/012056
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Machine learning and particularly algorithms based on artificial neural networks establishes a field of research lying at the intersection of different disciplines such as mathematics, statistics, computer science, and neuroscience. This approach is characterized by the utilization of algorithms to extract knowledge from large and heterogeneous data sets. A neural network technique is played to implement machine learning or to design intelligent machines for constructing mathematical models that can perform various complicated tasks. The set of machine learning algorithms have modernized and structured for fluid flows. It is helped to develop flow modeling and improvement techniques using neural networks the biologically impressed algorithms, current lines of mechanics research, and industrial applications.