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

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: see text] via the Weeks method at [Formula: see text] 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: see text] 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.

On Fundamental Solution for Autonomous Linear Retarded Functional Differential Equations

This document focuses attention on the fundamental solution of an autonomous linear retarded functional differential equation (RFDE) along with its supporting cast of actors: kernel matrix, characteristic matrix, resolvent matrix; and the Laplace transform. The fundamental solution is presented in the form of the convolutional powers of the kernel matrix in the manner of a convolutional exponential matrix function. The fundamental solution combined with a solution representation gives an exact expression in explicit form for the solution of an RFDE. Algebraic graph theory is applied to the RFDE in the form of a weighted loop-digraph to illuminate the system structure and system dynamics and to identify the strong and weak components. Examples are provided in the document to elucidate the behavior of the fundamental solution. The paper introduces fundamental solutions of other functional differential equations.

On Fundamental Solution for Autonomous Linear Retarded Functional Differential Equations

This document focuses attention on the fundamental solution of an autonomous linear retarded functional differential equation (RFDE) along with its supporting cast of actors: kernel matrix, characteristic matrix, resolvent matrix, and the Laplace transform. The fundamental solution is presented in a form of the convolutional powers of the kernel matrix in the manner of a convolutional exponential matrix function. The fundamental solution combined with a solution representation gives an exact expression in explicit form for the solution of a RFDE. Algebraic graph theory is applied to the RFDE in the form of a weighted loop-digraph to illuminate the system structure and system dynamics and to identify the strong and weak components. Examples are provided in the document to elucidate the behavior of the fundamental solution. The paper introduces fundamental solutions of other functional differential equations.

Eigenvalue sensitivity, singular values and discrete frequency selection mechanism in noise amplifiers: the case of flow induced by radial wall injection

We have performed linearized direct numerical simulations (DNS) of flow induced by radial wall injection forced by white-noise Gaussian forcings. We have shown that the frequency spectrum of the flow exhibits low-frequency discrete peaks in the case of a spatial structure of the forcing that is large scale. On the other hand, we observed that the spectrum becomes smooth (with no discrete peaks) if the spatial structure of the forcing is of a smaller extent. We have then tried to analyse these results in the light of global stability analyses. We have first computed the eigenvalue spectrum of the Jacobian and shown that the computed eigenvalues in the frequency range of interest were strongly damped and extremely sensitive to numerical discretization choices if large domains in the axial direction were considered. If shorter domains are used, then the eigenvalues are more robust but still extremely sensitive to the location of the upstream and downstream boundaries. Analysis of the$\def \xmlpi #1{}\def \mathsfbi #1{\boldsymbol {\mathsf {#1}}}\let \le =\leqslant \let \leq =\leqslant \let \ge =\geqslant \let \geq =\geqslant \def \Pr {\mathit {Pr}}\def \Fr {\mathit {Fr}}\def \Rey {\mathit {Re}}\epsilon $-pseudo-spectrum showed that eigenvalues located in a region displaying ‘background’ values of$ \epsilon $below$10^{-12}$were extremely sensitive and confirmed that all values in this region of the spectrum were actually quasi-eigenvalues. The eigenvalues are therefore ill-behaved and cannot be invoked to explain the observed discrete frequency selection mechanism. We have then performed a singular value decomposition of the global resolvent matrix to compute the leading optimal gains, optimal forcings and optimal responses, which are robust quantities, insensitive to numerical discretization details. We showed that the frequency response of the flow with the large-scale forcing can accurately be reproduced by an approximation based on the leading optimal gain/forcing/response. Analysis of this approximation showed that it is the projection coefficient of the forcing onto the leading optimal forcing that is responsible for the discrete frequency selection mechanism in the case of the large-scale forcing. From a more physical point of view, such a discrete behaviour stems from the streamwise oscillations of the leading optimal forcings, whose wavelengths vary with frequency, in combination with finite extent forcings (which start or end at locations where the leading optimal forcings are strong). Experimental results in the literature are finally discussed in light of these findings.