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researcher

Sébastien Kiesgen de Richter

2 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • last author2

Across the 2 of 2 papers where every author was matched, so the position is known.

fields
  • physics.flu-dyn2
ORCID 0000-0002-7513-6709

identity via Semantic Scholar / OpenAlex

most citedPhysics-informed neural networks for gravity currents reconstruction from limited data

24 citations · 25 across the 2 of their papers we have counts for

collaborators

2 papers

physics.flu-dyn2024★ 1 cited

Identification of Settling Velocity with Physics Informed Neural Networks For Sediment Laden Flows

Mickaël Delcey, Yoann Cheny, Jean-Baptiste Keck +2

Physics-Informed Neural Networks (PINNs) have shown great potential in the context of fluid dynamics simulations, particularly in reconstructing flow fields and identifying key par…

physics.flu-dyn2022★ 24 cited

Physics-informed neural networks for gravity currents reconstruction from limited data

Mickaël Delcey, Yoann Cheny, Sébastien Kiesgen de Richter

The present work investigates the use of physics-informed neural networks (PINNs) for the 3D reconstruction of unsteady gravity currents from limited data. In the PINN context, the…

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