Accuracy Improvements for Convolutional and Differential Distance Function Approximations
This work addresses a domain-specific problem in computational geometry or numerical analysis, likely incremental as it builds on existing schemes.
The paper tackled the problem of estimating distance functions from internal points to a domain's boundary, proposing accuracy improvements for convolutional and differential schemes using asymptotics of Laplace integrals and Taylor series extrapolations, with results evaluated but no concrete numbers provided.
Given a bounded domain, we deal with the problem of estimating the distance function from the internal points of the domain to the boundary of the domain. Convolutional and differential distance estimation schemes are considered and, for both the schemes, accuracy improvements are proposed and evaluated. Asymptotics of Laplace integrals and Taylor series extrapolations are used to achieve the improvements.