Some minor clarifications: NeuralODEs are not a Julia invention. I am pretty sure the first papers on the topic were using a python package implementing a rather crude ODE solver in torch or tensorflow. Julia just happens to be light years ahead of any other tool when it comes to solving ODEs, while having many high-quality autodifferentiation packages as well, so it feels natural to use it for these problems. But more importantly, SciML is not just for your typical Machine Learning tasks: being able to solve ODEs and have autodiff over them is incredibly empowering for boring old science and engineering, and SciML has become one of the most popular set of libraries when it comes to unwieldy ODEs.