  We demonstrate the power of 2D tensor networks for obtaining large deviation
functions of dynamical observables in a classical nonequilibrium setting. Using
these methods, we analyze the previously unstudied dynamical phase behavior of
the fully 2D asymmetric simple exclusion process with biases in both the x and
y directions. We identify a dynamical phase transition, from a jammed to a
flowing phase, and characterize the phases and the transition, with an estimate
of the critical point and exponents.
