Using Neural Networks to Approximate Distance of Possible Solutions of Uncertain Poisson Equation
Year of publication
2025
Authors
Halonen, Vilho; Pölönen, Ilkka; Wolfmayr, Monika
Abstract
We create and test a neural network which quantifies uncertainty errors in the Poisson equation generated by an uncertain source term. The neural networks performance is compared to a Monte Carlo approximation and analytically derived bounds. We find that with a suitable dataset the neural network can learn this task well enough to be considered as an alternative for Monte Carlo and analytical methods.
Show moreOrganizations and authors
JAMK University of Applied Sciences
Wolfmayr Monika 
Publication type
Publication format
Article
Parent publication type
Conference
Article type
Other article
Audience
ScientificPeer-reviewed
Peer-ReviewedMINEDU's publication type classification code
A4 Article in conference proceedingsPublication channel information
Journal/Series
Parent publication name
Publisher
Pages
396-405
ISSN
ISBN
Publication forum
Publication forum level
1
Open access
Open access in the publisher’s service
No
Self-archived
Yes
Other information
Fields of science
Computer and information sciences
Keywords
[object Object],[object Object],[object Object]
Publication country
Switzerland
Internationality of the publisher
International
Language
English
International co-publication
No
Co-publication with a company
No
DOI
10.1007/978-3-031-86173-4_40
The publication is included in the Ministry of Education and Culture’s Publication data collection
Yes