banamonster1
> my graduate work was using constraint optimizers in molecular dynamics
me too for qm/mm sims -- did some rg/complex systems work too ;)
the author is talking about constraint optimization in physics vs ml/ai
constraint optimization is easier with a well defined prior vs the guess work in ml algos.
dl models in physics can scale and emergent phenomena will depend on scaling parameters -- this is coarse graining in physics.
physics is also mostly deterministic (attach probability distributions for stochastic/quantum stuff) and there are well defined rules (energy, symmetry, noether, etc).
at the end of the day ai has some space for models and so does physics. because physics has well defined rules it's easier to apply constraints to that space vs ai/ml where it's informed guesswork.
of course there will be a correspondence between parameters in a model and emergent physical phenomena ... and i'm sure really nice scaling laws, etc will come out , this is just coarse graining.
onsager and the likes were onto this stuff way before deep learning was a thing. i think this connection is uninteresting because optimization in it's heart is physics. dl is just one aspect.
- a physicist (escaped to the greener pastures of swe, shame really, i miss it but not the wl balance)