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PINNProv: Provenance for Physics-Informed Neural Networks:
, In:
2023 International Symposium on Computer Architecture and High Performance Computing Workshops (SBAC-PADW)
,
de Oliveira, Lyncoln S.
;
Kunstmann, Liliane
;
Pina, Debora
.. - p. 16-23 , 2023
Link:
https://doi.org/10.1109/SBAC-PADW60351.2023.00013
RT T1
2023 International Symposium on Computer Architecture and High Performance Computing Workshops (SBAC-PADW)
: T1
PINNProv: Provenance for Physics-Informed Neural Networks
UL https://suche.suub.uni-bremen.de/peid=ieee-10306106&Exemplar=1&LAN=DE A1 de Oliveira, Lyncoln S. A1 Kunstmann, Liliane A1 Pina, Debora A1 de Oliveira, Daniel A1 Mattoso, Marta YR 2023 K1 Training K1 Computational modeling K1 High performance computing K1 Neural networks K1 Distributed databases K1 Production K1 Data models K1 provenance K1 physics-informed neural network SP 16 OP 23 LK http://dx.doi.org/https://doi.org/10.1109/SBAC-PADW60351.2023.00013 DO https://doi.org/10.1109/SBAC-PADW60351.2023.00013 SF ELIB - SuUB Bremen
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