Extrapolating Venusian Atmospheric Profiles using MAGMA Gaussian Processes

dc.contributor.author	Lejoly, S.
dc.contributor.author	Piccialli, A.
dc.contributor.author	Mahieux, A.
dc.contributor.author	Vandaele, A.C.,
dc.contributor.author	Frénay, B.
dc.date	2024
dc.date.accessioned	2024-09-13T13:11:13Z
dc.date.available	2024-09-13T13:11:13Z
dc.identifier.isbn	9782875870902
dc.identifier.uri	https://orfeo.belnet.be/handle/internal/13440
dc.description	In the field of spatial aeronomy, atmospheric profile datasets often contain partial data. Probabilistic models, particularly Gaussian processes (GPs), offer promising solutions for filling these data gaps. However, traditional GP algorithms encounter challenges when handling multiple sequences simultaneously, both in terms of performance and computational complexity. Recently, an algorithm named MAGMA was introduced to address these issues. This paper evaluates MAGMA’s performance using the SOIR Venus atmosphere dataset, marking the first application of MAGMA to atmospheric profiles. Results indicate that MAGMA represents a significant advancement towards the efficient application of GPs for extrapolating atmospheric profiles.
dc.language	eng
dc.title	Extrapolating Venusian Atmospheric Profiles using MAGMA Gaussian Processes
dc.type	Conference
dc.subject.frascati	Physical sciences
dc.audience	Scientific
dc.source.title	ESANN 2024 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, Belgium, 9-11 October 2024
dc.source.page	155-160
Orfeo.peerreviewed	No
dc.identifier.url