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Today, Owl can be used to solve a wide range of scientific problems: it provides efficient types for handling multidimensional arrays and linear algebra operations built on top of BLAS and LAPACK; it supports machine learning applications with a powerful computational graph engine and automatic differentiation pipeline. To improve efficiency, Owl allows offloading computations to distributed systems and GPUs. With the recent addition of dataframes and integration with Jupyter Notebooks provided by ocaml-jupyter, Owl has the chance to become an excellent framework for exploratory mathematical analysis.

OwlDE: making ODEs first-class Owl citizens