Interpretable Feature Extraction for the Numerical Particle System
S. Ren,
X. Zhang,
H. Li
et al.
Abstract:Particle system analysis is important but costly in solving many physical problems since particles are the basic composition of almost everything, and machine learning is increasingly used in optimization for numerical simulations. The most important and difficult job is to make the particle system understandable by the machine learning model, which usually called feature extraction. In this paper, a novel method and an accurate physical interpretation for feature extraction of cascade defects data, an importa… Show more
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