A deep learning approximation of non-stationary solutions to wave kinetic equations
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 213-226 |
Journal / Publication | Applied Numerical Mathematics |
Volume | 199 |
Online published | 23 Dec 2022 |
Publication status | Published - May 2024 |
Link(s)
Abstract
We present a deep learning approximation, stochastic optimization based, method for wave kinetic equations. To build confidence in our approach, we apply the method to a Smoluchowski coagulation equation with multiplicative kernel for which an analytic solution exists. Our deep learning approach is then used to approximate the non-stationary solution to a 3-wave kinetic equation corresponding to acoustic wave systems. To validate the neural network approximation, we compare the decay rate of the total energy with previously obtained theoretical results. A finite volume solution is presented and compared with the present method.
Research Area(s)
- 3-wave equation, Deep learning, Function approximation, Partial differential equations, Stochastic optimization, Wave turbulence
Bibliographic Note
Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).
Citation Format(s)
A deep learning approximation of non-stationary solutions to wave kinetic equations. / Walton, Steven; Tran, Minh-Binh; Bensoussan, Alain.
In: Applied Numerical Mathematics, Vol. 199, 05.2024, p. 213-226.
In: Applied Numerical Mathematics, Vol. 199, 05.2024, p. 213-226.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review