 Dear Fellow Scholars, this is two-minute papers with Karojona Ifehir. There are two main branches of efficient smoke and fluid simulator programs, Ilarian and Lagrangian techniques. Before we dive into what these terms mean, I'd like to note that we have close captions available for this series that you can turn on by clicking the CC button at the bottom of the player. With that out of the way, the Ilarian technique means that we have a fixed grid and the measurement happens in the grid points only. We have no idea what happens between these grid points. It may sound counterintuitive at first because it has no notion of particles at all. With the Lagrangian technique, we have particles that move around in space and we measure important quantities like velocity and pressure with these particles. In short, Ilarian, grids, Lagrangian, particles. Normally the problem with Ilarian simulations is that we don't know what exactly happens between the grid points, causing information to disappear in these regions. To alleviate this, they are usually combined with Lagrangian techniques because if we can also track all these particles individually, we cannot lose any of them. The drawback is of course that we need to simulate millions of particles which will take at least a few minutes for every frame we wish to compute. By formulating his famous equation, the Austrian physicist, Erwin Schrodinger, won the Nobel Prize in 1933. In case you're wondering, yes, this is the guy who forgot to fit his cat. There's two important things you should know about the Schrodinger equation. One is that it is used to describe how subatomic particles behave in time and two, it has absolutely nothing to do with large scale fluid simulations whatsoever. The point of this work is to reformulate Schrodinger's equation in a way that it tracks the density and the velocity of the fluid in time. This way it can be integrated in a purely grid-based Eulerian fluid simulator. And we don't need to track all these individual particles one by one, but we can still keep these fine, small scale details in a way that rivals Lagrangian simulations but without the huge additional costs. So the idea is absolutely bonkers, just the thought of doing this sounds so outlandish to me. And it works. Obstacles are also supported by this technique. Many questions still remain, such as how to mix different fluid interfaces together, how to model the forces between them. I do not have the pressure to see the limits of the approach, but I am quite convinced that this direction holds a lot of promise for the future. I cannot wait to play with the code and see some follow-up works on this. As always, everything is linked in the video description box. The paper is not only absolutely beautifully written, but it is also a really fun paper to read. And as I read it, I really loved how a jolt of epiphany ran through me. It is a fantastic feeling when a light bulb lights up in my mind as I suddenly get to understand something. I think it is the scientist's equivalent of obtaining enlightenment. May it happen to you fellow scholars often during your journeys. And I get to spend quite a bit of time every day reading fine works like this. It's a good life. I'd like to give a quick shout out to this really cool website called Short Science, which is a collection of crowdsourced short summaries for scientific papers. Really cool stuff. Make sure to have a look. Thanks for watching and for your generous support and I'll see you next time.