5 papers
A perfectly matched layer approach for the spectral split-step Padé method
Daniel Walsken, Matthias Ehrhardt, Pavel Petrov
The split-step-Padé (SSP) method is widely used to model wave phenomena in various applications, including radio physics, optics and acoustics. In this method, the propagator of t…
A Spectral Split-Step Padé Method for Guided Wave Propagation
Daniel Walsken, Pavel Petrov, Matthias Ehrhardt
In this study, a Fourier-based, split-step Padé (SSP) method for solving the parabolic wave equation with applications in guided wave propagation in ocean acoustics is presented.…
Port-Hamiltonian Neural Networks: From Theory to Simulation of Interconnected Stochastic Systems
Luca Di Persio, Matthias Ehrhardt, Youness Outaleb +1
This work introduces a new framework integrating port-Hamiltonian systems (PHS) and neural network architectures. This framework bridges the gap between deterministic and stochasti…
A finite element-based machine learning model for hydro-mechanical analysis of swelling behavior in clay-sulfate rocks
Reza Taherdangkoo, Mostafa Mollaali, Matthias Ehrhardt +4
The hydro-mechanical behavior of clay-sulfate rocks, especially their swelling properties, poses significant challenges in geotechnical engineering. This study presents a hybrid co…
A Space Mapping approach for the calibration of financial models with the application to the Heston model
Anna Clevenhaus, Claudia Totzeck, Matthias Ehrhardt
We present a novel approach for parameter calibration of the Heston model for pricing an Asian put option, namely space mapping. Since few parameters of the Heston model can be dir…