Optimal parameter selection in Weeks’ method for numerical Laplace transform inversion based on machine learning

Patrick O. Kano, Moysey Brio, Jacob Bailey

Research output: Contribution to journalArticlepeer-review

Abstract

The Weeks method for the numerical inversion of the Laplace transform utilizes a Möbius transformation which is parameterized by two real quantities, σ and b. Proper selection of these parameters depends highly on the Laplace space function F(s) and is generally a nontrivial task. In this paper, a convolutional neural network is trained to determine optimal values for these parameters for the specific case of the matrix exponential. The matrix exponential eA is estimated by numerically inverting the corresponding resolvent matrix (Formula presented.) via the Weeks method at (Formula presented.) pairs provided by the network. For illustration, classes of square real matrices of size three to six are studied. For these small matrices, the Cayley-Hamilton theorem and rational approximations can be utilized to obtain values to compare with the results from the network derived estimates. The network learned by minimizing the error of the matrix exponentials from the Weeks method over a large data set spanning (Formula presented.) pairs. Network training using the Jacobi identity as a metric was found to yield a self-contained approach that does not require a truth matrix exponential for comparison.

Original languageEnglish (US)
JournalJournal of Algorithms and Computational Technology
Volume15
DOIs
StatePublished - 2021

Keywords

  • Numerical Laplace transform inversion
  • Weeks’ method
  • machine learning
  • matrix exponential

ASJC Scopus subject areas

  • Numerical Analysis
  • Computational Mathematics
  • Applied Mathematics

Fingerprint

Dive into the research topics of 'Optimal parameter selection in Weeks’ method for numerical Laplace transform inversion based on machine learning'. Together they form a unique fingerprint.

Cite this