This tool already exists in Python and R. See https://cran.r-project.org/web/packages/diffeqr/index.html https://cran.r-project.org/web/packages/diffeqr/index.h

This tool already exists in Python and R. See https://cran.r-project.org/web/packages/diffeqr/index.html https://cran.r-project.org/web/packages/diffeqr/index.html and https://anaconda.org/conda-forge/diffeqpy https://anaconda.org/conda-forge/diffeqpy. Julia has language bindings that make it simple enough to bind to other high level languages that these projects are maintained by the core team and supports lots of the library, including forms of automatic differentiation and GPU kernel generation. See for example the bindings that allow for usage within PyTorch https://github.com/SciML/juliatorch https://github.com/SciML/juliatorch and the Python-based Collab notebooks showing the GPU usage https://colab.research.google.com/drive/1bnQMdNvg0AL-LyPcXBiH10jBij5QUmtY?usp=sharing https://colab.research.google.com/drive/1bnQMdNvg0AL-LyPcXBi.... With Julia v1.12's small binary generation, we plan to release forms via binaries with C ABIs over the next year as well. Are those sufficient or should we consider supporting other deployments?