  This paper introduces a new series of methods which combine modal
decomposition algorithms, such as singular value decomposition and high-order
singular value decomposition, and deep learning architectures to repair,
enhance, and increase the quality and precision of numerical and experimental
data. A combination of two- and three-dimensional, numerical and experimental
dasasets are used to demonstrate the reconstruction capacity of the presented
methods, showing that these methods can be used to reconstruct any type of
dataset, showing outstanding results when applied to highly complex data, which
is noisy. The combination of benefits of these techniques results in a series
of data-driven methods which are capable of repairing and/or enhancing the
resolution of a dataset by identifying the underlying physics that define the
data, which is incomplete or under-resolved, filtering any existing noise.
These methods and the Python codes are included in the first release of
ModelFLOWs-app.
