Ali Darijani

Ali Darijani

Computational Mathematics, Applied Mathematics

Currently I focus on developing a Geometric Gaussian Mixture program within the SFB1574/CRC1574 project.

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Closed-Form Gaussian Mixture Representations of d-Dimensional Triangulated Domains

Ali Darijani, Benedikt Stratmann, Ratan Bahadur Thapa, Uwe D. Hanebeck, Jürgen Beyerer, Nehal Afifi
Accepted to 2026 IEEE Sensor Data Fusion: Trends, Solutions, Applications (SDF) • 2026
Abstract
We derive a Gaussian Mixture (GM) representation of a full-dimensional subset of Euclidean space with finite volume from a finite simplicial triangulation in closed form. On each simplex, we replace the uniform probability density function (PDF) with a Gaussian PDF that minimizes the Kullback–Leibler (KL) divergence, arriving at the information projection onto the family of Gaussian PDFs. By exploiting affine isomorphisms between simplices, the joint convexity and bijection invariance properties of the KL divergence, identities for Dirichlet PDFs, and a partition-of-unity argument, we establish a dimension-dependent but geometry-independent sharp upper bound on the global KL divergence between the resulting GM PDF and the uniform PDF on the original domain. Numerical examples illustrate the construction and demonstrate its potential in practical applications.
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