Seeking technical feedback on a physics inspired learning theory

I’ve posted a short preprint on zenodo proposing a thermodynamic and Hamiltonian formulation of learning dynamics, aimed at explaining and unifying common optimization algorithms from minimal assumptions.

Using a quasi-static thermodynamic framework and symplectic geometry, equations of learning are derived and methods such as momentum based SGD can be recovered from the formalism, only from a few asumptions.

I would like to submit this work for publication, but I currently my advisor cannot review it since it's out of the scope of his expertise, he doesn't have the time and is about to retire, so I would value any high level technical feedback.

I'm a student and don't have experience publishing a paper, so any guidance would be very valued.

Preprint (DOI): https://doi.org/10.5281/zenodo.18071680
Repository: https://github.com/eric-hermosis/learning-dynamics
Project page: https://eric-hermosis.github.io/machine-learning/learning-dynamics/