Hi, my name is Roberta and I am a PhD student in astrophysics. This is my work. It's called the first AI simulation of a black hole. Our goal is to investigate the use of deep learning for a forecast state of turbulent flows onto black holes. We need one to understand black holes' physics. We want to answer two questions. The one of them is can the player learn black holes' physics? And if the answer is yes, we want to see if the player can be the new method to speed up numeric simulation from black holes. This is the motivation of our work. We want to investigate a new method to speed up the simulations. Because numeric simulations, they are the traditional methods. But they take too long time to simulate black holes' physics. Like a very simple treatment with a dynamical treatment. It takes seven days running 400 CPU cores. Which is a lot. So we need to investigate a new method. In this is the motivation of our work. The new method we want to investigate is the deep learning. We use convolutional neural networks as our main goal. The data, they are Navier-Stokes solutions. But we need to consider black holes' gravitational potential. So we put up a black hole in the solutions of these Navier-Stokes equations. And we have three main parameters. The data is the pressure and the velocity. Is the result we get from Navier-Stokes solutions. We have two case. One of them is the one simulation case where the model learns from own system only. Learns from this only system and simulates this only system. In the multi simulation case, we fit eight different systems. But the physics is the same. The only difference between the initial conditions between them. We put this in the model. And the model start, you want the model to predict a system the model never saw before. So it simulates a never seen system. OK. This is the result of the one simulation case. Here's the direct predictions where we fit a simulation frame to the model and the model predicts. So we fit another simulation frame and predicts. We see that the error is very slow. It's below 1%, but we see some problems in the polar region and the outer boundaries due to the data's resolution. This is where they take the paper. But in general, the solution of the model is really well. It's almost the same between the targets and the prediction. And the second here is the terative prediction where we just fit a single simulation frame to the model and the model predicts. And then we pick this prediction. We fit again to the model and this goes on and on and on and on. And we see that the model can learn in two a certain time. But after that, the error goes really quickly. So this is something to investigate as well. Here is the multi-simulation case. The case, the model never saw before. So we see that the model can simulate by itself OK. We put 2.3% in general, the model can simulate. And it learns the physics very well. Again, the detail on the paper. So the one question is answered already. We see that the model, the deployment can be this new method. But we want to see if this can speed up the merke simulations. And the answer is yes. In the most simulation case, the speed up is 32,000 times faster than the merke simulations. And the never seen system before the speed up is 7,000. The result why the never seen system is a little bit lower than the simulation case is because the train set is larger. So the train time is also larger as well. But in both cases, they are faster than the merke simulation. OK. Here is just a quickly show how all the simulations are. And the model can predict very well all of them. OK. So it doesn't have any problems. Here's the conclusion. We see that machine learning or especially deployment can learn a simulation of Christian disk and can be used to understand black holes physics. It simulates well to a start to almost 1,000 gravitational time. It generalized to a never seen system. And again, the details on my paper and in my master's thesis. One of limitations is the data resolution. So in now, I want to investigate this in my PhD. OK. Thank you very much. This is my email. This is my Twitter. You can just reach out to me there. OK. Thanks so much. Bye.