Ebeida, Mohamed S.Rushdi, Ahmad A.Awad, Muhammad A.Mahmoud, Ahmed H.Yan, Dong-MingEnglish, Shawn A.Owens, John D.Bajaj, Chandrajit L.Mitchell, Scott A.Maks Ovsjanikov and Daniele Panozzo2016-06-172016-06-1720161467-8659https://doi.org/10.1111/cgf.12981We introduce an algorithmic framework for tuning the spatial density of disks in a maximal random packing, without changing the sizing function or radii of disks. Starting from any maximal random packing such as a Maximal Poisson-disk Sampling (MPS), we iteratively relocate, inject (add), or eject (remove) disks, using a set of three successively more-aggressive local operations. We may achieve a user-defined density, either more dense or more sparse, almost up to the theoretical structured limits. The tuned samples are conflict-free, retain coverage maximality, and, except in the extremes, retain the blue noise randomness properties of the input. We change the density of the packing one disk at a time, maintaining the minimum disk separation distance and the maximum domain coverage distance required of any maximal packing. These properties are local, and we can handle spatially-varying sizing functions. Using fewer points to satisfy a sizing function improves the efficiency of some applications. We apply the framework to improve the quality of meshes, removing non-obtuse angles; and to more accurately model fiber reinforced polymers for elastic and failure simulations.10.1111/cgf.12981259-269