Versenden
Drucken
Juliust, Hessel und Elsen, Katharina Maria und Schady, Arthur und Gharbi, Sirine und Dietrich, Felix (2024) Data-Driven and Physics-Informed Machine Learning for Outdoor Acoustic Wave Modeling using the Linearized Euler Equations. In: Data-Driven and Physics-Informed Machine Learning for Outdoor Acoustic Wave Modeling using the Linearized Euler Equations. DAS/DAGA 2025 - 51st Annual Meeting on Acoustics, 2024-03-17 - 2024-03-20, Copenhagen, Denmark. doi: 10.71568/dasdaga2025.630.
![[img]](https://elib.dlr.de/style/images/fileicons/application_pdf.png) |
PDF
2MB |
Offizielle URL: https://doi.org/10.71568/dasdaga2025.630
Kurzfassung
Modeling of outdoor acoustic wave propagation is crucial for diverse applications, including noise mapping for urban planning [1], environmental monitoring [2], and aviation acoustics [3]. The propagation of acoustic waves outdoors is strongly influenced by atmospheric conditions, such as wind and temperature gradients [2], and interactions with topographic features, making accurate predictions challenging.
| elib-URL des Eintrags: | https://elib.dlr.de/214529/ | ||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dokumentart: | Konferenzbeitrag (Poster) | ||||||||||||||||||||||||
| Titel: | Data-Driven and Physics-Informed Machine Learning for Outdoor Acoustic Wave Modeling using the Linearized Euler Equations | ||||||||||||||||||||||||
| Autoren: |
| ||||||||||||||||||||||||
| Datum: | 1 Mai 2024 | ||||||||||||||||||||||||
| Erschienen in: | Data-Driven and Physics-Informed Machine Learning for Outdoor Acoustic Wave Modeling using the Linearized Euler Equations | ||||||||||||||||||||||||
| Referierte Publikation: | Ja | ||||||||||||||||||||||||
| Open Access: | Ja | ||||||||||||||||||||||||
| Gold Open Access: | Nein | ||||||||||||||||||||||||
| In SCOPUS: | Nein | ||||||||||||||||||||||||
| In ISI Web of Science: | Nein | ||||||||||||||||||||||||
| DOI: | 10.71568/dasdaga2025.630 | ||||||||||||||||||||||||
| Status: | veröffentlicht | ||||||||||||||||||||||||
| Stichwörter: | Outdoor Acoustic Propagation, Linearized Euler Equations (LEE), Physics-Informed Neural Networks (PINNs), Fourier Neural Operators (FNOs), Machine Learning in Acoustics | ||||||||||||||||||||||||
| Veranstaltungstitel: | DAS/DAGA 2025 - 51st Annual Meeting on Acoustics | ||||||||||||||||||||||||
| Veranstaltungsort: | Copenhagen, Denmark | ||||||||||||||||||||||||
| Veranstaltungsart: | internationale Konferenz | ||||||||||||||||||||||||
| Veranstaltungsbeginn: | 17 März 2024 | ||||||||||||||||||||||||
| Veranstaltungsende: | 20 März 2024 | ||||||||||||||||||||||||
| Veranstalter : | German and Danish Acoustical Societies, DEGA and DAS | ||||||||||||||||||||||||
| HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||||||||||
| HGF - Programm: | Raumfahrt | ||||||||||||||||||||||||
| HGF - Programmthema: | Erdbeobachtung | ||||||||||||||||||||||||
| DLR - Schwerpunkt: | Raumfahrt | ||||||||||||||||||||||||
| DLR - Forschungsgebiet: | R EO - Erdbeobachtung | ||||||||||||||||||||||||
| DLR - Teilgebiet (Projekt, Vorhaben): | R - Atmosphären- und Klimaforschung | ||||||||||||||||||||||||
| Standort: | Oberpfaffenhofen | ||||||||||||||||||||||||
| Institute & Einrichtungen: | Institut für Physik der Atmosphäre > Angewandte Meteorologie | ||||||||||||||||||||||||
| Hinterlegt von: | Juliust, Hessel | ||||||||||||||||||||||||
| Hinterlegt am: | 10 Jun 2025 10:33 | ||||||||||||||||||||||||
| Letzte Änderung: | 10 Jun 2025 10:33 |
Nur für Mitarbeiter des Archivs: Kontrollseite des Eintrags
