# Liquid Splash Modeling With Neural Networks

## Метаданные

- **Канал:** Two Minute Papers
- **YouTube:** https://www.youtube.com/watch?v=OV0ivJB2lyI
- **Дата:** 17.03.2019
- **Длительность:** 3:43
- **Просмотры:** 57,718
- **Источник:** https://ekstraktznaniy.ru/video/14343

## Описание

❤️ This video has been kindly supported by my friends at Arm Research. Check them out here! - http://bit.ly/2TqOWAu

📝 The paper "Liquid Splash Modeling with Neural Networks" is available here:
https://ge.in.tum.de/publications/2018-mlflip-um/

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## Транскрипт

### <Untitled Chapter 1> []

dear fellow Scholars this is 2minute peers with car 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 to 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 this 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 GameChanger 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 course simulation quickly that surely aderes 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 infus simulation program on the left by the name ml flip introduces these amazing details and if we compare the results

### Comparison with high resolution FLIP [2:42]

with the reference simulation which took forever you can 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

### Training data generation 2 [2:55]

droplets flying about you guessed it right by looking get 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 speed up from this so This basically means

### Comparison with FLIP [3:16]

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 see you next time