 Dear Fellow Scholars, this is two-minute papers with Karuijona Ife here. If we study the laws of fluid motion from physics and write a computer program that contains these laws, we can create wondrous fluid simulations like the ones you see here. The amount of detail we can simulate with these programs is increasing every year, not only due to the fact that hardware improves over time, but also the pace of progress in computer graphics research is truly remarkable. So, is there nothing else to do? Are we done with fluid simulation research? Oh no! No, no, no. For instance, fluid solid interaction still remains a challenging phenomenon to simulate. This means that the sand is allowed to have an effect on the fluid, but at the same time, as the fluid sloshes around, it also moves the sand particles within. This is what we refer to as two-way coupling. Note that this previous work that you see here was built on the material point method, a hybrid simulation technique that uses both particles and grids, whereas this new paper introduces proper fluid solid coupling to the simpler grid-based simulators. Not only that, but this new work also shows us that there are different kinds of two-way coupling. If we look at this footage with the honey and the deeper, it looks great, however, this still doesn't seem right to me. We are doing science here, so fortunately, we don't need to guess what seems and what doesn't seem right. This is my favorite part because this is when we let reality BR judge and compare to what exactly happens in the real world. So, let's do that. Whoa! There's quite a bit of a difference because in reality, the honey is able to support the deeper. One-way coupling, of course, cannot simulate this kind of back and forth interaction and neither can weak two-way coupling pull this off. And now, let's see. Yes, there we go. The new strong two-way coupling method finally gets this right. And not only that, but what I really love about this is that it also gets small nuances right. I will try to speed up the footage a little so you can see that the honey doesn't only support the deeper, but the deeper still has some subtle movements both in reality and in the simulation. A plus, love it. So, what is the problem? Why is this so difficult to simulate? One of the key problems here is being able to have a simulation that has a fine resolution in areas where fluid and the solid intersect each other. If we create a super detailed simulation, it will take from hours to days to compute. But on the other hand, if we have a two-course one, it will compute the required deformations in so few of these grid points that will get a really inaccurate simulation and not only that, but we will even miss some of the interactions completely. This paper proposes a neat new volume estimation technique that focuses these computations to where the action happens and only there, which means that we can get these really incredible results even if we only run a relatively coarse simulation. I could watch these GUI viscous simulations all day long. If you have a closer look at the paper in the description, you will find some hard data that shows that this technique executes quicker than other methods that are able to provide comparable results. This episode has been supported by weights and biases. Weight and biases provides tools to track your experiments in your deep learning projects. It can save you a ton of time and money in these projects and is being used by OpenAI, Toyota Research, Stanford and Berkeley. In this blog post, you see that with the help of weights and biases, it is possible to write an AI that plays the witness one of my favorite puzzler games. If you are interested in the game itself, you can also check out my earlier video on it. I know it sounds curious. I indeed made a video about a game on this channel. You can find it in the video description. And also, make sure to visit them through www.wndb.com slash papers or just click the link in the video description and sign up for a freedom of today. Or thanks to weights and biases for helping us make better videos for you. Thanks for watching and for your generous support and I'll see you next time.