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people are missing the point the author is talking about constraint optimization in physics vs ml/ai constraint optimization is easier with a well defined pri
by banamonster1 5y ago
people are missing the point
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.
- dekhn 5y agoI didn't miss the point. my graduate work was using constraint optimizers in molecular dynamics (n-body physics) and it translated to ML (I didn't have to relearn anything). The one part that was truly simpler is the objective functions in ML are believable to be convex, while n-body physics with constraints are highly non-convex).
- banamonster1 5y agopeople on the board, not you > my graduate work was using constraint optimizers in molecular dynamics me too for qm/mm sims -- did some rg/complex systems work too ;)
- slipframe 5y agoIn the future you should probably respond to the people you intend your comment to be a response to.