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Mariella Kast

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Related publications (4)

Page 1 of 1

Galerkin neural network-POD for acoustic and electromagnetic wave propagation in parametric domains

Jan Sickmann Hesthaven, Mariella Kast, Philipp Christoph Weder

We investigate reduced order models for acoustic and electromagnetic wave problems in parametrically defined domains. The parameter-to-solution maps are approximated following the so-called Galerkin POD-NN method, which combines the construction of a reduc ...

2025

A non-intrusive multifidelity method for the reduced order modeling of nonlinear problems

Jan Sickmann Hesthaven, Mengwu Guo, Mariella Kast

We propose a non-intrusive reduced basis (RB) method for parametrized nonlinear partial differential equations (PDEs) that leverages models of different accuracy. From a collection of low-fidelity (LF) snapshots, parameter locations are extracted for the e ...

ELSEVIER SCIENCE SA2020

Positional Embeddings for Solving PDEs with Evolutional Deep Neural Networks

Jan Sickmann Hesthaven, Mariella Kast

This work extends the paradigm of evolutional deep neural networks (EDNNs) to solving parametric time -dependent partial differential equations (PDEs) on domains with geometric structure. By introducing positional embeddings based on eigenfunctions of the ...

2024

Leveraging Contrast Agent Kinetics for Robust Reflectance Mode Fluorescence Tomography

Jan Sickmann Hesthaven, Mariella Kast

Fluorescence Image Guided Surgery utilizes continuous wave epi-fluorescence measurements on the tissue surface to locate targets such as tumors or lymph nodes, but precise 3D localization of deep targets remains intractable due to the illposedness of the a ...

IEEE2025