How to make Black Holes eat Neutron stars in a supercomputer and why bother?

• Sebastian Gomez Lopez

The extraction of physical observables from realistic merger simulations of black holes and/or neutron stars is a cornerstone of present and future gravitational wave observations and studies of electromagnetic emissions from the dynamical ejecta produced in events where neutron stars are tidally disrupted. As no complete closed-form solutions for the two body problem in general relativity have been reported in the literature for the complete inspiral-merger-postmerger evolution, powerful supercomputing clus- ters and codes highly parallelized to take advantage of hundreds/thousands of CPUs have been used to evolve these systems. This talk will offer an introductory description of the 3+1 Numerical relativity implementation used in public codes like the Einstein toolkit. In particular, It aims to describe how the space-time domain is treated to make this type of simulation possible and explain qualitatively the method of Gravitational wave extraction using the Weyl scalar.

Dicussion

The Hopfield model towards modern generalizations

• Claudio Chilin

The Hopfield model is a fully connected neural network of biological inspiration that aims to reproduce an associative memory. The most important contribution on the topic is the phase diagram obtained via the replica trick, which makes this system one of the few analytically treatable model of neural network. The scope of an associative memory is somehow different from that of most deep learning networks. Despite this, we can observe some phenomenological similarities with modern machine learning concepts. This might suggest that this system could be of key interest for a general theory of neural computation. During the seminar we will also discuss some recent generalizations of the model that narrow the gap with the deep neural networks we are used to. Specifically, we will talk about the use of structured data and we will take a look at the DayDreaming algorithm that can improve the network capacity up to its theoretical limit.

Discussion