Physics-Informed Neural State Space Models via Learning and Evolution

Recent works exploring deep learning application to dynamical systems modeling have demonstrated that embedding physical priors into neural networks can yield more effective, physically-realistic, and data-efficient models. However, in the absence of complete prior knowledge of a dynamical system�...

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Hauptverfasser:	Skomski, Elliott, Drgona, Jan, Tuor, Aaron
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Neural and Evolutionary Computing
Online-Zugang:	Volltext bestellen
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