259 citations · 978 across the 60 of their papers we have counts for
9 papers · 1 filter
AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier-Stokes Solutions
Florent Bonnet, Ahmed Jocelyn Mazari, Paola Cinnella +1
Surrogate models are necessary to optimize meaningful quantities in physical dynamics as their recursive numerical resolutions are often prohibitively expensive. It is mainly the c…
HMOE: Hypernetwork-based Mixture of Experts for Domain Generalization
Jingang Qu, Thibault Faney, Ze Wang +3
Due to domain shifts, machine learning systems typically struggle to generalize well to new domains that differ from those of training data, which is what domain generalization (DG…
Block-wise Training of Residual Networks via the Minimizing Movement Scheme
Skander Karkar, Ibrahim Ayed, Emmanuel de Bézenac +1
End-to-end backpropagation has a few shortcomings: it requires loading the entire model during training, which can be impossible in constrained settings, and suffers from three loc…
Continuous PDE Dynamics Forecasting with Implicit Neural Representations
Yuan Yin, Matthieu Kirchmeyer, Jean-Yves Franceschi +2
Effective data-driven PDE forecasting methods often rely on fixed spatial and / or temporal discretizations. This raises limitations in real-world applications like weather predict…
An extensible Benchmarking Graph-Mesh dataset for studying Steady-State Incompressible Navier-Stokes Equations
Florent Bonnet, Jocelyn Ahmed Mazari, Thibaut Munzer +2
Recent progress in \emph{Geometric Deep Learning} (GDL) has shown its potential to provide powerful data-driven models. This gives momentum to explore new methods for learning phys…
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…
PTFlash : A deep learning framework for isothermal two-phase equilibrium calculations
Jingang Qu, Thibault Faney, Jean-Charles de Hemptinne +2
Phase equilibrium calculations are an essential part of numerical simulations of multi-component multi-phase flow in porous media, accounting for the largest share of the computati…
Diverse Weight Averaging for Out-of-Distribution Generalization
Alexandre Ramé, Matthieu Kirchmeyer, Thibaud Rahier +3
Standard neural networks struggle to generalize under distribution shifts in computer vision. Fortunately, combining multiple networks can consistently improve out-of-distribution…
Generalizing to New Physical Systems via Context-Informed Dynamics Model
Matthieu Kirchmeyer, Yuan Yin, Jérémie Donà +3
Data-driven approaches to modeling physical systems fail to generalize to unseen systems that share the same general dynamics with the learning domain, but correspond to different…