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Mathematics > Numerical Analysis

arXiv:2206.10292 (math)
[Submitted on 21 Jun 2022 (v1), last revised 5 Oct 2022 (this version, v2)]

Title:Artificial Neural Network evaluation of Poincaré constant for Voronoi polygons

Authors:Beatrice Crippa, Silvia Bertoluzza, Micol Pennacchio
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Abstract:We propose a method, based on Artificial Neural Networks, that learns the dependence of the constant in the Poincaré inequality on polygonal elements of Voronoi meshes, on some geometrical metrics of the element. The cost of this kind of algorithms mainly resides in the data preprocessing and learning phases, that can be performed offline once and for all, constructing an efficient method for computing the constant, which is needed in the design of a posteriori error estimates in numerical mesh-based schemes for the solution of Partial Differential Equations.
Subjects: Numerical Analysis (math.NA)
Cite as: arXiv:2206.10292 [math.NA]
  (or arXiv:2206.10292v2 [math.NA] for this version)
  https://doi.org/10.48550/arXiv.2206.10292

Submission history

From: Micol Pennacchio [view email]
[v1] Tue, 21 Jun 2022 12:17:04 UTC (1,277 KB)
[v2] Wed, 5 Oct 2022 14:47:15 UTC (1,277 KB)
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