3 citations · 7 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 2 cited
INFINITY: Neural Field Modeling for Reynolds-Averaged Navier-Stokes Equations
Louis Serrano, Leon Migus, Yuan Yin +2
For numerical design, the development of efficient and accurate surrogate models is paramount. They allow us to approximate complex physical phenomena, thereby reducing the computa…
cs.LG2023★ 2 cited
Stability of implicit neural networks for long-term forecasting in dynamical systems
Leon Migus, Julien Salomon, Patrick Gallinari
Forecasting physical signals in long time range is among the most challenging tasks in Partial Differential Equations (PDEs) research. To circumvent limitations of traditional solv…
cs.LG2022★ 3 cited
Multi-scale Physical Representations for Approximating PDE Solutions with Graph Neural Operators
Léon Migus, Yuan Yin, Jocelyn Ahmed Mazari +1
Representing physical signals at different scales is among the most challenging problems in engineering. Several multi-scale modeling tools have been developed to describe physical…