Scipediacontent: Scipediacontent moved page Draft Content 447985184 to Dutta et al 2021a

2021-07-12T06:41:34Z

Scipediacontent moved page Draft Content 447985184 to Dutta et al 2021a

Scipediacontent: Created page with "== Abstract == Model reduction for fluid flow simulation continues to be of great interest across a number of scientific and engineering fields. In a previous work [1], we ex..."

2021-07-12T06:41:31Z

Created page with "== Abstract == Model reduction for fluid flow simulation continues to be of great interest across a number of scientific and engineering fields. In a previous work [1], we ex..."

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== Abstract ==

Model reduction for fluid flow simulation continues to be of great interest across a number of scientific and engineering fields. In a previous work [1], we explored the use of Neural Ordinary Differential Equations (NODE) as a non-intrusive method for propagating the latent-space dynamics in reduced order models. Here, we investigate employing deep autoencoders for discovering the reduced basis representation, the dynamics of which are then approximated by NODE. The ability of deep autoencoders to represent the latent-space is compared to the traditional proper orthogonal decomposition (POD) approach, again in conjunction with NODE for capturing the dynamics. Additionally, we compare their behavior with two classical non-intrusive methods based on POD and radial basis function interpolation as well as dynamic mode decomposition. The test problems we consider include incompressible flow around a cylinder as well as a real-world application of shallow water hydrodynamics in an estuarine system. Our findings indicate that deep autoencoders can leverage nonlinear manifold learning to achieve a highly efficient compression of spatial information and define a latentspace that appears to be more suitable for capturing the temporal dynamics through the NODE framework.

== Full document ==
<pdf>Media:Draft_Content_447985184P_IDC13_954.pdf</pdf>

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Dutta et al 2021a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 447985184 to Dutta et al 2021a

Scipediacontent: Created page with "== Abstract == Model reduction for fluid flow simulation continues to be of great interest across a number of scientific and engineering fields. In a previous work [1], we ex..."