  Digital network twin (DNT) is a promising paradigm to replicate real-world
cellular networks toward continual assessment, proactive management, and
what-if analysis. Existing discussions have been focusing on using only deep
learning techniques to build DNTs, which raises widespread concerns regarding
their generalization, explainability, and transparency. In this paper, we
explore an alternative approach to augment network simulators with
context-aware neural agents. The main challenge lies in the non-trivial
simulation-to-reality (sim-to-real) discrepancy between offline simulators and
real-world networks. To solve the challenge, we propose a new learn-to-bridge
algorithm to cost-efficiently bridge the sim-to-real discrepancy in two
alternative stages. In the first stage, we select states to query performances
in real-world networks by using newly-designed cost-aware Bayesian
optimization. In the second stage, we train the neural agent to learn the state
context and bridge the probabilistic discrepancy based on Bayesian neural
networks (BNN). In addition, we build a small-scale end-to-end network testbed
based on OpenAirInterface RAN and Core with USRP B210 and a smartphone, and
replicate the network in NS-3. The evaluation results show that, our proposed
solution substantially outperforms existing methods, with more than 92\%
reduction in the sim-to-real discrepancy.
