| Original language | English |
|---|---|
| Article number | 110586 |
| Number of pages | 20 |
| Journal | Journal of Computational Physics |
| Volume | 444 |
| DOIs | |
| Publication status | Published - 1 Nov 2021 |
Funding
We acknowledge financial support by the Austrian Science Foundation ( FWF ) via the projects “ROAM” under grant No. P31140-N32 and the SFB “Complexity in PDEs” under grant No. F65 . The financial support by the Austrian Federal Ministry for Digital and Economic Affairs , the National Foundation for Research, Technology and Development and the Christian Doppler Research Association is gratefully acknowledged. The authors acknowledge the Wiener Wissenschafts und Technologie Fonds ( WWTF ) project No. MA16-066 (“SEQUEX”) and the University of Vienna research platform MMM Mathematics - Magnetism - Materials. The computations were partly achieved by using the Vienna Scientific Cluster (VSC) via the funded project No. 71140 .
Austrian Fields of Science 2012
- 101014 Numerical mathematics
- 103043 Computational physics
- 102019 Machine learning
Keywords
- Low-rank kernel approximation
- Low-rank kernel principal component analysis
- Machine learning
- Micromagnetics
- Nonlinear model order reduction
- Nystroem approximation
- INTEGRATION
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Dive into the research topics of 'Prediction of magnetization dynamics in a reduced dimensional feature space setting utilizing a low-rank kernel method'. Together they form a unique fingerprint.Projects
- 1 Finished
-
CD-Labor Advanced Magnetic Sensing and Materials
Süss, D. (Project Lead) & Vranckx Herrera, S. E. (Admin)
1/05/17 → 31/07/20
Project: Research cooperation
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