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ZENODO
Software . 2020
License: CC BY
Data sources: Datacite
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ZENODO
Software . 2020
License: CC BY
Data sources: ZENODO
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datafold: data-driven models for point clouds and time series on manifolds

Authors: Lehmberg, Daniel; Dietrich, Felix; Köster, Gerta; Bungartz, Hans-Joachim;

datafold: data-driven models for point clouds and time series on manifolds

Abstract

The archive of the software datafold is for publication in "The Journal of Open Source Software" (JOSS). The review is documented here: https://github.com/openjournals/joss-reviews/issues/2283 datafold is a Python package that provides data-driven models for point clouds to find an explicit mani-fold parametrization and to identify non-linear dynamical systems on these manifolds. Informally, a manifold is an usually unknown geometrical structure on which data is sampled. The explicit treatment of a manifold allows prior knowledge of a system and its problem-specific domain to be included. This can be the proximity between points in the dataset or functions defined on the phase space manifold of a dynamical system, such as (partially) known governing equation terms.

Keywords

manifold learning, dynamic mode decomposition, time series, data-driven models, dynamical system, point cloud, Python

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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