In: 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada, pp. North-Holland, Amsterdam (2000)īalestriero, R., Cosentino, R., Aazhang, B., and Baraniuk R.G.: The Geometry of deep networks: power diagram subdivision. A survey of a fundamental geometric data structure. arXiv:1907.04050Īsh, P.F., Bolker, E.D.: Generalized Dirichlet tessellations. We briefly describe these results paying more attention to the practical application of the Voronoi tessellation related to the spatial large-scale galaxy distribution.Īmbrogioni, L., Güçlü, U., van Gerven, M.: k-GANs: ensemble of generative models with semi-discrete optimal transport (2019). The very innovative results were obtained in astronomy, namely for a large-scale galaxy distribution and cosmic web pattern, for revealing the quasi-periodicity in a pencil-beam survey, for a description of constraints on the isotropic cosmic microwave background and the explosion scenario likely supernova events, for image processing, adaptive smoothing, segmentation, for signal-to-noise ratio balancing, for spectrography data analysis as well as in the moving-mesh cosmology simulation.
The varieties of the Voronoi tessellation methods are commonly used in computational fluid dynamics, computational geometry, geolocation and logistics, game dev programming, cartography, engineering, liquid crystal electronic technology, machine learning, etc. The Voronoi tessellation is a natural way of space segmentation, which has many applications in various fields of science and technology as well as in social sciences and visual art.
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