  From Physics and Biology to Seismology and Economics, the behaviour of
countless systems is determined by impactful yet unlikely transitions between
metastable states known as \emph{rare events}, the study of which is essential
for understanding and controlling these systems' properties. Classical
computational methods to sample rare events remain prohibitively inefficient,
and are a bottleneck for enhanced samplers requiring prior data. Here, we
introduce a novel framework, FlowRES that uses unsupervised normalising flow
neural networks to enhance Monte Carlo sampling of rare events by generating
high-quality nonlocal Monte Carlo proposals. We validate FlowRES by sampling
the transition path ensembles of equilibrium and non-equilibrium systems of
Brownian particles exploring increasingly complex potential surfaces. Beyond
eliminating requirements for prior data, FlowRES features key advantages over
established samplers: no collective variables need defining, its efficiency
remains constant even as events become increasingly unlikely, and it can handle
systems with multiple routes between states.
