Speaker
Andrei Alexandru
(The George Washington University)
Description
Machine learning can be used for generative methods that approximate PDFs corresponding to quantum field theories. To remove any bias an accept/reject step is required which for theories with fermions involve the calculation of the fermionic determinant. We investigate the use of pseudo-fermion methods for the accept/reject step to bypass the need to compute costly determinants. As an example we use the two dimensional Thirring model.