GANs for generating EFT models
Harold Erbin, Sven Krippendorf
Abstract
We initiate a way of generating effective field theories (EFT) models by the computer, satisfying both experimental and theoretical constraints. We use Generative Adversarial Networks (GAN) and display generated instances which go beyond the examples known to the machine during training. As a starting point, we apply this idea to the generation of supersymmetric field theories with a single field. We find cases where the number of minima in the generated scalar potential is different from values found in the training data. We comment on potential further applications of this framework.
Topics & Concepts
Maxima and minimaGenerative grammarField (mathematics)Computer sciencePoint (geometry)Adversarial systemScalar fieldScalar (mathematics)Theoretical computer scienceTheoretical physicsMachine learningArtificial intelligenceMathematicsPhysicsPure mathematicsQuantum mechanicsGeometryMathematical analysisComputational Physics and Python ApplicationsModel Reduction and Neural NetworksGenerative Adversarial Networks and Image Synthesis