Bio-inspired multi-dimensional deep fusion learning for predicting dynamical aerospace propulsion sy...
Bio-inspired multi-dimensional deep fusion learning for predicting dynamical aerospace propulsion systems
About this item
Full title
Author / Creator
Publisher
London: Nature Publishing Group UK
Journal title
Language
English
Formats
Publication information
Publisher
London: Nature Publishing Group UK
Subjects
More information
Scope and Contents
Contents
Rapid and precise forecasting of dynamical systems is critical to ensuring safe aerospace missions. Previous forecasting research has primarily concentrated on global trend analysis using full-scale inputs. However, time series arising from real-world applications such as aerospace propulsion, exhibit a distinct dynamical periodicity over a limited...
Alternative Titles
Full title
Bio-inspired multi-dimensional deep fusion learning for predicting dynamical aerospace propulsion systems
Authors, Artists and Contributors
Author / Creator
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_8d8eac9b9218443ba5d33700b785bfc8
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_8d8eac9b9218443ba5d33700b785bfc8
Other Identifiers
ISSN
2731-3395
E-ISSN
2731-3395
DOI
10.1038/s44172-024-00327-9
