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Terence Tao

๐Ÿ‘ค Person
2047 total appearances

Appearances Over Time

Podcast Appearances

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

So sometimes these forces are in balance at small scales, but not in balance at large scales or vice versa.

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

So Navier-Stokes is what's called supercritical.

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

So at smaller and smaller scales,

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

the transport terms are much stronger than the viscosity terms.

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

So the viscosity terms are the things that calm things down.

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

And so this is why the problem is hard.

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

In two dimensions, so the Soviet mathematician Ladislav Skaya, she, in the 60s, showed in two dimensions there was no blow-up.

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

And as you mentioned, the Navier-Stokes equation is what's called critical.

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

The effect of transport and the effect of viscosity are about the same strength, even at very, very small scales.

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

And we have a lot of technology to handle critical and also subcritical equations and improve regularity.

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

But for supercritical equations, it was not clear what was going on.

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

and i did a lot of work and then there's been a lot of follow-up showing that for many other types of super critical equations you can create all kinds of blow-up examples once the non-linear effects dominate the linear effects at small scales you can have all kinds of bad things happen so this is sort of one of the main insights of this this line of work is that super criticality versus criticality and sub-criticality this this makes a big difference i mean that's a key qualitative feature that distinguishes

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

Some equations were being sort of nice and predictable and, you know, like planetary motion.

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

I mean, there's certain equations that you can predict for millions of years or thousands at least.

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

Again, it's not really a problem, but there's a reason why we can't predict the weather past two weeks into the future because it's a super critical equation.

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

Lots of really strange things are going on at very fine scales.

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

Yeah, and if non-linearity is somehow more and more featured and interesting at small scales.

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

There's many equations that are non-linear, but in many equations you can approximate things by the bulk.

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

For example, planetary motion, if you want to understand the orbit of the Moon or Mars or something, you don't really need the microstructure of the seismology of the Moon or exactly how the mass is distributed.

Lex Fridman Podcast
#472 โ€“ Terence Tao: Hardest Problems in Mathematics, Physics & the Future of AI

You can almost approximate these planets by point masses.