An optimal control framework for adaptive neural ODEs

In recent years, the notion of neural ODEs has connected deep learning with the field of ODEs and optimal control. In this setting, neural networks are defined as the mapping induced by the corresponding time-discretization scheme of a given ODE. The learning task consists in finding the ODE paramet...

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Veröffentlicht in:	Advances in computational mathematics Jg. 50; H. 3; S. 52
Hauptverfasser:	Aghili, Joubine, Mula, Olga
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.06.2024 Springer Nature B.V
Schlagworte:	Accuracy Adaptive sampling Algorithms Approximation Cognitive tasks Computational mathematics Computational Mathematics and Numerical Analysis Computational Science and Engineering Deep learning Discretization Dynamical systems Mathematical and Computational Biology Mathematical Modeling and Industrial Mathematics Mathematics Mathematics and Statistics Neural networks Optimal control Optimization Sampling error Visualization Deep learning Adaptivity 68T05 Optimal control 65K10 68Q32 65L10 49J15 Neural ODE networks
ISSN:	1019-7168, 1572-9044
Online-Zugang:	Volltext
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