GANs for generating EFT models
This provides a novel computational approach for physicists to generate consistent theoretical models, though it appears incremental as it builds on existing GAN methods for a specific domain application.
The researchers tackled the problem of generating effective field theory models that satisfy both experimental and theoretical constraints by developing a framework using Generative Adversarial Networks (GANs). They applied this to supersymmetric field theories, where the machine generated new examples with distinct properties like numbers of minima not present in the training data.
We initiate a way of generating models by the computer, satisfying both experimental and theoretical constraints. In particular, we present a framework which allows the generation of effective field theories. We use Generative Adversarial Networks to generate these models and we generate examples which go beyond the examples known to the machine. As a starting point, we apply this idea to the generation of supersymmetric field theories. In this case, the machine knows consistent examples of supersymmetric field theories with a single field and generates new examples of such theories. In the generated potentials we find distinct properties, here the number of minima in the scalar potential, with values not found in the training data. We comment on potential further applications of this framework.