The Noise Handling Properties of the Talbot Algorithm for Numerically Inverting the Laplace Transform
For researchers solving time-dependent differential equations via Laplace transforms, this provides a practical advantage in noisy data scenarios, though the finding is incremental.
This paper evaluates the noise handling properties of three Laplace transform inversion algorithms, finding that the Talbot algorithm significantly outperforms Fourier Series and Stehfest methods in handling noisy data.
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 performs much better than the Fourier Series and Stehfest numerical inversion schemes as 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.