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Inferring topological transitions in pattern-forming processes with self-supervised learning. (arXiv:2203.10204v2 [cond-mat.mtrl-sci] UPDATED)
cs.CV updates on arXiv.org arxiv.org
The identification and classification of transitions in topological and
microstructural regimes in pattern-forming processes are critical for
understanding and fabricating microstructurally precise novel materials in many
application domains. Unfortunately, relevant microstructure transitions may
depend on process parameters in subtle and complex ways that are not captured
by the classic theory of phase transition. While supervised machine learning
methods may be useful for identifying transition regimes, they need labels
which require prior knowledge of order parameters or relevant structures
describing these …
arxiv learning processes self-supervised learning supervised learning transitions