Abstract
This paper examines the noise handling properties of three of the most widely used algorithms for numerically inverting the Laplace Transform. After examining the genesis of the algorithms,
the regularization properties are evaluated through a series of standard test functions in which noise is added to the inverse transform.
Comparisons are then made with the exact data. Our main finding is that the Talbot inversion algorithm is very good at handling noisy data and is more accurate than the Fourier Series and Stehfest numerical inversion schemes as they are outlined in this paper. This offers a considerable advantage for it's use in inverting the Laplace Transform when seeking numerical solutions to time dependent differential equations.
the regularization properties are evaluated through a series of standard test functions in which noise is added to the inverse transform.
Comparisons are then made with the exact data. Our main finding is that the Talbot inversion algorithm is very good at handling noisy data and is more accurate than the Fourier Series and Stehfest numerical inversion schemes as they are outlined in this paper. This offers a considerable advantage for it's use in inverting the Laplace Transform when seeking numerical solutions to time dependent differential equations.
| Original language | English |
|---|---|
| Pages | 1-27 |
| Number of pages | 27 |
| DOIs | |
| Publication status | Published - 13 Sept 2018 |