  Hamilton-Jacobi reachability (HJR) provides a value function that encodes the
set of states from which a system with bounded control inputs can reach or
avoid a target despite any bounded disturbance, and the corresponding robust,
optimal control policy. Though powerful, traditional methods for HJR rely on
dynamic programming (DP) and suffer from exponential computation growth with
respect to state dimension. The recently favored Hopf formula mitigates this
``curse of dimensionality'' by providing an efficient and space-parallelizable
approach for solving the reachability problem. However, the Hopf formula can
only be applied to linear time-varying systems. To overcome this limitation, we
show that the error between a nonlinear system and a linear model can be
transformed into an adversarial bounded artificial disturbance. One may then
solve the dimension-robust generalized Hopf formula for a linear game with this
``antagonistic error" to perform guaranteed conservative reachability analysis
and control synthesis of nonlinear systems; this can be done for problem
formulations in which no other HJR method is both computationally feasible and
guaranteed. In addition, we offer several technical methods for reducing
conservativeness in the analysis. We demonstrate the effectiveness of our
results through one illustrative example (the controlled Van der Pol system)
that can be compared to standard DP, and one higher-dimensional 15D example (a
5-agent pursuit-evasion game with Dubins cars).
