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Droplet impacts are of fundamental importance to the natural water cycle as collision and coalescence of droplets are the primary mechanism by which warm rain forms. Additionally, droplet impacts are of paramount significance in a variety of industrial processes, including spray cooling, wet scrubbing, and even play a role in cooling nuclear reactors. Throughout this work, we utilize a combination of theoretical mod- eling and experiments to elucidate the dynamics of these common phenomena. The first problem we analyze is a droplet impacting a deep fluid bath. Millimetric drops are generated using a piezoelectric droplet-on-demand generator and normally impact a bath of the same fluid. The limit where capillarity and fluid inertia dominate the interfacial dynamics is investigated. This so-called inertio-capillary limit is shown to define an upper bound on the possible coefficient of restitution for droplet–bath im- pact. We then consider the scenario where the substrate is no longer deformable, and study the dynamics of non-wetting droplets impacting on stationary and vibrating substrates, with deterministic chaos emerging in the latter case. Extending beyond axisymmetric impacts, we then analyze droplet impact scenarios where there is some relative tangential velocity between the substrate and droplet. We determine the thresholds for coalescence, and our results suggest that substrate deformability plays an important role in transitions between the bouncing and merging regimes. Finally, we consider an analogous solid case to the normal droplet impact studies, where a small rigid sphere impacts and rebounds from a deformable elastic membrane. We perform experiments and identify new previously unreported behaviors in this simple system which are attributed to the non-negligible inertia of the membrane. Overall, the dynamics of these impact scenarios are extremely rich, producing physical phe- nomena that are inherently multi-scale, requiring information and knowledge from micron to centimeter scales. As showcased here, reduced-order modeling and con- trolled experiments are essential in distilling some simplicity out of the complexity.