New page released

I just made a tutorial to build and install all the dependencies to run the Deep Learning network I'm studying for fluid simulation, right here. For now, I can just execute it and visualize the results. The presentation video can be remade on your computer if you follow all the steps, no skipping or changing the order (please).

Soon I'm gonna find out the next step.

If you are interested on this topic and you want to work with Disney, check out the lib this project uses: Partio. And also, the SPlisH SPlasH that uses it.

I'm Back

Hello there. Almost a year, huh? 

Last time I stopped because I needed to generate data for a paper, which is now at the last step to be accepted. It is about Rigid Registration with Lie Groups. When it is released, I'll post here.

Since then, I started to research about Deep Learning for this topic. But I found out it is very complicated when you don't know much of the theory. 

For instance, I made a Toy Model with a Multilayer Perceptron to compute the triangle area. However, I couldn't make it have a good accuracy. 

So the task was assigned to another student who knows better about neural networks, and he concluded that triangles with very small area are being interpreted as outliers by the network, and making the training harder. Also, there is an error accumulation due to numerical instability, so the training needed 64 bit float and the error measurement, a 32 bit float.

Right now, I changed my research to fluid simulation with Deep Learning, far more interesting to me. I just made this code work, and very soon I'll post the step by step to make it work for you too.

See ya.