What is it about?

Numerous disciplines within the physical sciences, including oceanography, geophysics, astrophysics, and magnetism, employ vector pattern (vector field) data to depict a wide array of physical phenomena. Categorizing these patterns into distinct classes allows for a comprehensive overview of the various phenomena observable within a system. This paper presents a method for organizing the vector patterns into such categories, harnessing the capabilities of Artificial Intelligence (AI). Specifically, it demonstrates this approach through the classification of vector patterns of magnetization in magnetic materials, serving as a case study. By doing so, it highlights the potential of AI to enhance our understanding and analysis of complex vector pattern data across multiple scientific domains.

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Why is it important?

The application of vector patterns for depicting physical phenomena is widespread, paralleled by the integration of Artificial Intelligence (AI) across various domains within the physical sciences. Our findings indicate that AI emerges as a formidable ally in automating vector pattern classification processes. This technique is characterized by its precision, speed, and minimal need for human intervention. By leveraging AI for classification, we can significantly streamline the labor-intensive and error-prone task of manually analyzing extensive vector pattern datasets.

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This page is a summary of: Machine learning based classification of vector field configurations, AIP Advances, February 2024, American Institute of Physics,
DOI: 10.1063/9.0000686.
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