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Parameterized Neural Ordinary Differential Equations [electronic resource] : Applications to Computational Physics Problems.

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Bibliographic Details
Online Access:	Full Text (via OSTI)
Corporate Author:	Sandia National Laboratories (Researcher)
Format:	Government Document Electronic eBook
Language:	English
Published:	Washington, D.C. : Oak Ridge, Tenn. : United States. National Nuclear Security Administration ; Distributed by the Office of Scientific and Technical Information, U.S. Department of Energy, 2020.
Subjects:	97 mathematics and computing 71 classical and quantum mechanics, general physics Model reduction Deep learning Autoencoders Machine learning Nonlinear manifolds Neural ordinary differential equations Latentdynamics learning Mathematics and computing Classical and quantum mechanics, general physics Latent-dynamics learning

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Full Text (via OSTI)

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Call Number:	E 1.99:SAND-2020-11835R
E 1.99:SAND-2020-11835R	Available