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Unsupervised Learning
Basic Concepts and Application to Particle Dynamics
Machine learning tools can be broadly categorized into three types: supervised, unsupervised and semi‐supervised learning. Unsupervised learning is useful in situations where labeled data are scarce, enabling insights from larger datasets. This chapter is devoted to the comprehensive description of the basic concepts and most popular techniques of unsupervised learning. It describes the two main branches of unsupervised learning: clustering and dimensionality reduction techniques. Unsupervised learning has numerous real‐world applications in many domains. As an example of applications, the chapter illustrates an application of unsupervised learning to the discovery of patterns in particles dynamics. A particular focus is made on large‐scale molecular dynamics simulations performed with up to 10 million atoms for the purpose of describing the early stages of solidification of a material, called homogeneous nucleation.
Unsupervised Learning
Basic Concepts and Application to Particle Dynamics
Machine learning tools can be broadly categorized into three types: supervised, unsupervised and semi‐supervised learning. Unsupervised learning is useful in situations where labeled data are scarce, enabling insights from larger datasets. This chapter is devoted to the comprehensive description of the basic concepts and most popular techniques of unsupervised learning. It describes the two main branches of unsupervised learning: clustering and dimensionality reduction techniques. Unsupervised learning has numerous real‐world applications in many domains. As an example of applications, the chapter illustrates an application of unsupervised learning to the discovery of patterns in particles dynamics. A particular focus is made on large‐scale molecular dynamics simulations performed with up to 10 million atoms for the purpose of describing the early stages of solidification of a material, called homogeneous nucleation.
Unsupervised Learning
Basic Concepts and Application to Particle Dynamics
Stefanou, Ioannis (author) / Darve, Félix (author) / JAKSE, Noel (author)
Machine Learning in Geomechanics 1 ; 93-115
2024-10-25
23 pages
Article/Chapter (Book)
Electronic Resource
English
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