Datafold: Data-driven Models for Point Clouds and Time Series on Manifolds

Ever increasing data availability has changed the way how data is analyzed and interpreted in many scientific fields. While the underlying complex systems remain the same, data measurements increase in both quantity and dimension. The main drivers are larger computer simulation capabilities and increasingly versatile sensors. In contrast to an equation-driven workflow, a scientist can use data-driven models to analyze a wider range of systems, including those with unknown or intractable equations. The models can be applied to a variety of data-driven scenarios, such as enriching the analysis of unknown systems or merely serve as an equation-free surrogate by providing fast, albeit approximate, responses to unseen data.

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