Search Results for author:
Found 4 papers, 2 papers with code
Automatic differentiation approach for reconstructing spectral functions with neural networks
Reconstructing spectral functions from Euclidean Green's functions is an important inverse problem in physics.
Reconstructing spectral functions via automatic differentiation
Exploiting the neural networks' regularization as a non-local smoothness regulator of the spectral function, we represent spectral functions by neural networks and use the propagator's reconstruction error to optimize the network parameters unsupervisedly.
The quantum kinetic equation and dynamical mass generation in 2+1 Dimensions
Within this framework, the parity-odd transport current induced by the external electromagnetic field is self-consistently derived.
High Energy Physics - Theory Quantum Physics
Effects of local event-by-event conservation laws in ultra-relativistic heavy ion collisions at the particlization
Many simulations of relativistic heavy ion collisions involve the switching from relativistic hydrodynamics to kinetic particle transport.
High Energy Physics - Phenomenology Nuclear Theory
