Abstract We build a field-level emulator for cosmic structure formation that is accurate in the nonlinear regime. Our consists of two convolutional neural networks trained to output displacements and velocities N -body simulation particles based on their linear inputs. Cosmology dependence encoded form style parameters at each layer network, enabling effectively interpolate outcomes between dif...
