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Reachability analysis is a fundamental problem in verification that checks for a given model and set of initial states if the system will reach a given set of unsafe states. Its importance lies in the ability to exhaustively explore the behaviors of a model over a finite or infinite time horizon. The problem of reachability analysis for Cyber-Physical Systems (CPS) is especially challenging because it involves reasoning about the continuous states of the system as well as its switching behavior. Each of these two aspects can by itself cause the reachability analysis problem to be undecidable. In this paper, we survey recent progress in this field beginning with the success of hybrid systems with affine dynamics. We then examine the current state-of-the-art for CPS with nonlinear dynamics and those driven by ``learning-enabled'' components such as neural networks. We conclude with an examination of some promising directions and open challenges.