To respond to both the parent question, and this comment: indeed, this is black-box optimal control in essence.
However, this method is just one small aspect of the SciML [0] ecosystem now. The article is a little outdated in that sense.
Once obtaining your NN control parameter, it's now possible to use Sparse Identification of Nonlinear Dynamics (SINDy) on that parameter to recover equations of motion governing it [1].
The real promise of these methods is to use the universal approximator power of NNs to get around the 'curse of dimensionality' & uncover presently unknown representations of motion within any system. Take a look at [2] for a more detailed description.
However, this method is just one small aspect of the SciML [0] ecosystem now. The article is a little outdated in that sense.
Once obtaining your NN control parameter, it's now possible to use Sparse Identification of Nonlinear Dynamics (SINDy) on that parameter to recover equations of motion governing it [1].
The real promise of these methods is to use the universal approximator power of NNs to get around the 'curse of dimensionality' & uncover presently unknown representations of motion within any system. Take a look at [2] for a more detailed description.
[0]: https://sciml.ai/ [1]: https://datadriven.sciml.ai/dev/sparse_identification/sindy/ [2]: https://arxiv.org/abs/2001.04385