  Control barrier certificates have proven effective in formally guaranteeing
the safety of the control systems. However, designing a control barrier
certificate is a time-consuming and computationally expensive endeavor that
requires expert input in the form of domain knowledge and mathematical
maturity. Additionally, when a system undergoes slight changes, the new
controller and its correctness certificate need to be recomputed, incurring
similar computational challenges as those faced during the design of the
original controller. Prior approaches have utilized transfer learning to
transfer safety guarantees in the form of a barrier certificate while
maintaining the control invariant. Unfortunately, in practical settings, the
source and the target environments often deviate substantially in their control
inputs, rendering the aforementioned approach impractical. To address this
challenge, we propose integrating \emph{inverse dynamics} -- a neural network
that suggests required action given a desired successor state -- of the target
system with the barrier certificate of the source system to provide formal
proof of safety. In addition, we propose a validity condition that, when met,
guarantees correctness of the controller. We demonstrate the effectiveness of
our approach through three case studies.
