Talk page
Title:
Learning to Inflate
Speaker:
Abstract:
Motivated by machine learning, we introduce a novel method for randomly generating inflationary potentials, treating the Taylor coefficients of the potential as weights in a single-layer neural network and using gradient ascent to maximize the number of e-folds of inflation. We study the phenomenology of the models along the gradient ascent trajectory, finding substantial agreement with experiment for large-field local maximum models and small-field inflection point models. We speculate on possible uses for machine learning in model-building.
Link:
Workshop: