Parameterized Neural Ordinary Differential Equations [electronic resource] : Applications to Computational Physics Problems.
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| Online Access: |
Full Text (via OSTI) |
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| 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.
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| Abstract: | This work proposes an extension of neural ordinary differential equations (NODEs) by introducing an additional set of ODE input parameters to NODEs. This extension allows NODEs to learn multiple dynamics specified by the input parameter instances. Our extension is inspired by the concept of parameterized ordinary differential equations, which are widely investigated in computational science and engineering contexts, where characteristics of the governing equations vary over the input parameters. We apply the proposed parameterized NODEs (PNODEs) for learning latent dynamics of complex dynamical processes that arise in computational physics, which is an essential component for enabling rapid numerical simulations for time-critical physics applications. For this, we propose an encoder-decoder-type framework, which models latent dynamics as PNODEs. We demonstrate the effectiveness of PNODEs with important benchmark problems from computational physics. |
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| Item Description: | Published through Scitech Connect. 11/06/2020. "SAND-2020-11835R." "Other: 691636." ": US2204977." Lee, Kookjin ; Parish, Eric Joshua ; |
| Physical Description: | Size: 19 p. : digital, PDF file. |