Dear Fellow Scholars, this is Two Minute Papers
with Károly Zsolnai-Fehér.
If you have been watching this series for
a while, you know that I am completely addicted
to fluid simulations, so it is now time for
a new fluid paper.
And by the end of this video, I hope you will
be addicted too.
If we create a virtual world with a solid
block, and use our knowledge from physics
to implement the laws of fluid dynamics, this
solid block will indeed start behaving like
a fluid.
A baseline simulation technique for this will
be referred to as FLIP in the videos that
you see here and it stands for Fluid Implicit
Particle.
These simulations are often being used in
the video game industry, in movies, and of
course, I cannot resist to put some of them
in my papers as test scenes as well.
In games, we are typically looking for real-time
simulations, and in this case, we can only
get a relatively coarse-resolution simulation
that lacks fine details, such as droplet formation
and splashing.
For movies, we want the highest-fidelity simulation
possible, with honey coiling, two-way interaction
with other objects, wet sand simulations,
and all of those goodies, however, these all
take forever to compute.
This is the bane of fluid simulators.
We have talked about a few earlier works that
try to learn these laws via a neural network
by feeding them a ton of video footage of
these phenomena.
This is absolutely amazing and is a true game
changer for learning-based techniques.
So, why is that?
Well, up until a few years ago, whenever we
had a problem that was near impossible to
solve with traditional techniques, we often
reached out to a neural network or some other
learning algorithm to solve it, often with
success.
However, it is not the case here.
Something has changed.
What has changed is that we can already solve
these problems, but we can still make use
of a neural network because it can help us
with something that we can already do, but
it does it faster and easier.
However, some of these techniques for fluids
are not yet as accurate as we would like and
therefore haven’t yet seen widespread adoption.
So here’s an incredible idea: why not compute
a coarse simulation quickly that surely adheres
to the laws of physics, and then, fill the
remaining details with a neural network.
Again, FLIP is the baseline hand-crafted technique,
and you can see how the neural network-infused
simulation program on the left by the name
MLFlip introduces these amazing details.
Hm-hm!
And if we compare the results with the reference
simulation, which took forever, you see that
it is quite similar and it indeed fills in
the right kind of details.
In case you are wondering about the training
data, it learned the concept of splashes and
droplets flying about … you guessed it right…by
looking at splashes and droplets flying about.
So, now we know that it’s quite accurate
- and now, the ultimate question is, how fast
is it?
Well, get this - we can expect a 10-time speedup
from this.
So this basically means that for every 10
all nighters I have to wait for my simulations,
I only have to wait one, and if something
took only a few seconds, it now may be close
to real time with this kind of visual fidelity.
You know what, sign me up.
This video has been kindly supported by my
friends at ARM Research, make sure to check
them out through the link in the video description.
Thanks for watching and for your generous
support, and I'll see you next time!
