Eric Kinner

"Streamline Embedding using Machine Learning"

Streamlines are used as one of the primary ways to visualize any kind of vectorfield. Various domains of engineering and research analyze their results in this way. A meaningful embedding of streamlines facilitates their workflow by automatically selecting, discarding, highlighting, compressing, or analysing the data efficiently. With advancements in machine learning it has become possible to create deep embeddings for even complex multivariate data. Using these techniques we explore their applicability on scientific visualization data to improve current research analysis pipelines.

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