Constructing PDFs of spatially dependent fields using finite elements
Paul M. Mannix, David A. Ham, John Craske
TL;DR
This paper introduces a finite element method for estimating the probability density function of spatially dependent fields, implemented in the Python package NumDF, enabling advanced statistical analysis of spatial data.
Contribution
It presents a novel finite element approach for constructing PDFs of spatial fields, along with a Python implementation in NumDF, facilitating practical applications.
Findings
Effective estimation of spatial PDFs demonstrated
Numerical implementation in Python validated
Method enhances statistical analysis of spatial data
Abstract
A probability density function (PDF) of a spatially dependent field provides a means of calculating moments of the field or, equivalently, the proportion of a spatial domain that is mapped to a given set of values. This paper describes a finite element approach to estimating the PDF of a spatially dependent field and its numerical implementation in the Python package NumDF.
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Taxonomy
TopicsComputational Geometry and Mesh Generation · Manufacturing Process and Optimization