The DeepRT project combines methods from theoretical physics and machine learning to tackle a central physics challenge that hampers progress in many areas, e.g. relativistic heavy-ion collisions. Our physics goal is to elucidate how energy and momentum are transferred among microscopic matter constituents in extreme conditions, where "extreme" may refer to high temperatures or density. It requires us to compute the transport coefficients bulk and shear viscosity. In quantum field theory, such real-time properties are encoded in spectral functions. Extracting those from standard first-principles Monte-Carlo simulations however amounts to an exponentially hard inverse problem. We aim to overcome this issue with an improved extraction (subproject I) and an alternative simulation prescription (subproject II). In subproject I a PhD student will apply the concept of autoencoder (AE), a pillar of the deep-learning revolution in image processing, to the extraction of spectral functions from Monte-Carlo simulations. The central task is to develop the required topology and training strategy for the neural network. A large data pool from previous work of the PI will be an essential training ingredient. The ability of an AE to represent relevant features of input in hidden layers will be used to obtain improved Bayesian regularization schemes for inverse problems. In subproject II a postdoc will combine deep learning with complex Langevin simulations to compute real-time properties directly, evading the inverse problem. The Langevin approach so far has suffered from instabilities, which we propose to overcome by a neural network that acts as a dynamical control system. Developing and training the network in both the non-interacting theory, where analytic data is available and from concurrent standard imaginary time simulations will be the central challenge. In case of gauge fields, the degeneracy in the field variables will require developing and handling of deep networks.
Project leader: Alexander Karl Rothkopf
Institution: Institutt for matematikk og fysikk