22 citations · 22 across the 2 of their papers we have counts for
5 papers
A duality connecting neural network and cosmological dynamics
Sven Krippendorf, Michael Spannowsky
We demonstrate that the dynamics of neural networks trained with gradient descent and the dynamics of scalar fields in a flat, vacuum energy dominated Universe are structurally pro…
Revealing systematics in phenomenologically viable flux vacua with reinforcement learning
Sven Krippendorf, Rene Kroepsch, Marc Syvaeri
The organising principles underlying the structure of phenomenologically viable string vacua can be accessed by sampling such vacua. In many cases this is prohibited by the computa…
Improving Simulations with Symmetry Control Neural Networks
Marc Syvaeri, Sven Krippendorf
The dynamics of physical systems is often constrained to lower dimensional sub-spaces due to the presence of conserved quantities. Here we propose a method to learn and exploit suc…
Integrability ex machina
Sven Krippendorf, Dieter Lust, Marc Syvaeri
Determining whether a dynamical system is integrable is generally a difficult task which is currently done on a case by case basis requiring large human input. Here we propose and…
Detecting Symmetries with Neural Networks
Sven Krippendorf, Marc Syvaeri
Identifying symmetries in data sets is generally difficult, but knowledge about them is crucial for efficient data handling. Here we present a method how neural networks can be use…