Bulletin of the American Physical Society
74th Annual Meeting of the APS Division of Fluid Dynamics
Volume 66, Number 17
Sunday–Tuesday, November 21–23, 2021; Phoenix Convention Center, Phoenix, Arizona
Session T24: Computational Fluid Dynamics: Algorithms II; Shock Capturing; SPH & Mesh Free Methods
12:40 PM–2:50 PM,
Tuesday, November 23, 2021
Room: North 224 B
Chair: Michael Chertkov, University of Arizona
Abstract: T24.00009 : A Multi-Block Neural Networks for General and Approximate Riemann problems.*
2:24 PM–2:37 PM
Presenter:
Huangsheng Wei
(Xi'an Jiaotong Univ)
Authors:
Huangsheng Wei
(Xi'an Jiaotong Univ)
Zhu Huang
(Xi'an Jiaotong Univ)
Guang Xi
(Xi'an Jiaotong Univ)
With the advantage of SPNNs, the spatio-temporal domain could be divided into multiple smooth spaces, which avoids the difficulties in representing non-analytic and discontinuous functions for neural network. Therefore, the MBNNs could be trained with lower depth and breadth given the number and types of discontinuities or shocks. The one-dimensional generalized Riemann problem has been approximated and the predictions of MBNNs agree very well with the numerical simulations for the one-dimensional shock problems.
*This work is supported by the National Natural Science Foundation of China (Grant Nos. 51790512).
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