
Deep learning of physical laws from scarce data - che_shr_cat
https://arxiv.org/abs/2005.03448
======
RhysU
2D incompressible Navier-Stokes ain't too shabby. The problems they
investigated should have been in the abstract.

3D compressible N.-S. where error is measured via some functional of interest
after direct numerical simulation of the discovered equations would be neat to
see. I am curious which conservation laws it could figure out.

------
api
I've suspected for a while that this is how we unlock the next phase in
theoretical physics, such as unifying QM and gravity. We may have hit some
kind of human cognitive limit or blind region, so maybe we need to deploy some
bigger (but more specialized) guns.

~~~
smaddox
Personally, I think Stephen Wolfram and his colleagues are on the right track
on that one: [https://writings.stephenwolfram.com/2020/04/finally-we-
may-h...](https://writings.stephenwolfram.com/2020/04/finally-we-may-have-a-
path-to-the-fundamental-theory-of-physics-and-its-beautiful/)

~~~
devalgo
I'd be wary of that. He needs to make some predictions that can be tested or
it's just another string theory/brane theory/many worlds/etc. theory that
makes a lot of claims but hasn't advanced the science at all. We need testable
predictions, preferably new particles in energy regimes we can actually reach
with current or near future tech.

Edit: There's a big danger in accepting theories because they have simplicity
or beauty. That's how we ended up with String Theory and Fundamental
Theoretical Physics has basically stalled since the '50s because of it.

~~~
orbifold
The Standard Model was formulated in the 70s.

