Quantifying uncertainty in neural network predictions of forced vibrations

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Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Bibliographic citation: In: Proceedings in Applied Mathematics and Mechanics : PAMM, 24, (4), Wiley-VCH

Classification: Wirtschaft

Event: Veröffentlichung

(where): Darmstadt

(who): Universitäts- und Landesbibliothek

(when): 2025

Creator: Westmeier, Tobias
Hetzler, Hartmut
Kreuter, Daniel Christopher
Seitz, Philipp
Chalvatzaki, Georgia

DOI: 10.26083/tuprints-00029030

URN: urn:nbn:de:tuda-tuprints-290303

Rights: Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:35 AM CEST

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Quantifying uncertainty in neural network predictions of forced vibrations

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Other Objects (12)

Quantifying uncertainty in neural network predictions of forced vibrations

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A Bayesian joint probability post-processor for reducing errors and quantifying uncertainty in monthly streamflow predictions

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Bifurcation analysis of a forced delay equation for machine tool vibrations

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Forced free vibrations of a square plate

A Bayesian joint probability post-processor for reducing errors and quantifying uncertainty in monthly streamflow predictions

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Data Driven prediction of forced nonlinear vibrations using stabilised Autoregressive Neural Networks

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