Domain Knowledge Assisted Gas Tomography

Thomas Wiedemann, Patrick Hinsen, Victor Prieto Ruiz, Dmitriy Shutin, Achim J. Lilienthal

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

Abstract

The presented work addresses the challenging problem of Gas Tomography (GT) – a reconstruction technique for a spatial gas distribution based on measurements with an open-path sensor. Given that such a sensor only captures an integrated gas concentration along a laser trajectory, the reconstruction problem is inherently ill-posed. The method proposed in this work assists GT reconstruction using domain knowledge in the form of a gas dispersion model, formally described by the advection-diffusion Partial Differential Equation (PDE). Specifically, the model facilitates a physics-informed interpolation in regions not covered by measurements. As a result, the proposed numerical approach demonstrates a considerably improved performance in simulation studies, as compared to state-of-the-art GT algorithms.

OriginalspracheEnglisch
TitelISOEN 2024 - International Symposium on Olfaction and Electronic Nose, Proceedings
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
ISBN (elektronisch)9798350348651
DOIs
PublikationsstatusVeröffentlicht - 2024
Veranstaltung2024 IEEE International Symposium on Olfaction and Electronic Nose, ISOEN 2024 - Grapevine, USA/Vereinigte Staaten
Dauer: 12 Mai 202415 Mai 2024

Publikationsreihe

NameISOEN 2024 - International Symposium on Olfaction and Electronic Nose, Proceedings

Konferenz

Konferenz2024 IEEE International Symposium on Olfaction and Electronic Nose, ISOEN 2024
Land/GebietUSA/Vereinigte Staaten
OrtGrapevine
Zeitraum12/05/2415/05/24

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