Targeting multi-loop integrals with neural networks

Numerical evaluations of Feynman integrals often proceed via a deformation the integration contour into complex plane. While valid contours are easy to construct, numerical precision for multi-loop integral can depend critically on chosen contour. We present methods optimize this using combination optimized, global shifts and normalizing flow. They lead significant gain in precision.