84 citations · 121 across the 13 of their papers we have counts for
14 papers
An End-to-End PyTorch Interface for Differentiable PDE Solvers: A RANS Model-Correction Study
Luca Saverio, Michele Alessandro Bucci, Gianmarco Farro +2
This work presents an end-to-end strategy for solving inverse problems constrained by Partial Differential Equations within a fully differentiable Machine Learning framework. The p…
PLAID: A Unified Data Model for Machine Learning on Heterogeneous Physics Simulations
Fabien Casenave, Xavier Roynard, Brian Staber +17
Machine learning-based surrogate models have emerged as a powerful tool to accelerate simulation-driven scientific workflows, but their adoption is limited by the lack of large-sca…
Mean flow data assimilation using physics-constrained Graph Neural Networks
M. Quattromini, M. A. Bucci, S. Cherubini +1
Despite their widespread use, purely data-driven methods often suffer from overfitting, lack of physical consistency, and high data dependency, particularly when physical constrain…
Multi-Level GNN Preconditioner for Solving Large Scale Problems
Matthieu Nastorg, Jean-Marc Gratien, Thibault Faney +3
Large-scale numerical simulations often come at the expense of daunting computations. High-Performance Computing has enhanced the process, but adapting legacy codes to leverage par…
Enhancing Data-Assimilation in CFD using Graph Neural Networks
Michele Quattromini, Michele Alessandro Bucci, Stefania Cherubini +1
We present a novel machine learning approach for data assimilation applied in fluid mechanics, based on adjoint-optimization augmented by Graph Neural Networks (GNNs) models. We co…
Active learning of data-assimilation closures using Graph Neural Networks
Michele Quattromini, Michele Alessandro Bucci, Stefania Cherubini +1
The spread of machine learning techniques coupled with the availability of high-quality experimental and numerical data has significantly advanced numerous applications in fluid me…
An Implicit GNN Solver for Poisson-like problems
Matthieu Nastorg, Michele Alessandro Bucci, Thibault Faney +3
This paper presents -GNN, a novel Graph Neural Network (GNN) approach for solving the ubiquitous Poisson PDE problems with mixed boundary conditions. By leveraging the Implicit…
Continuous Methods : Adaptively intrusive reduced order model closure
Emmanuel Menier, Michele Alessandro Bucci, Mouadh Yagoubi +4
Reduced order modeling methods are often used as a mean to reduce simulation costs in industrial applications. Despite their computational advantages, reduced order models (ROMs) o…
DS-GPS : A Deep Statistical Graph Poisson Solver (for faster CFD simulations)
Matthieu Nastorg, Marc Schoenauer, Guillaume Charpiat +3
This paper proposes a novel Machine Learning-based approach to solve a Poisson problem with mixed boundary conditions. Leveraging Graph Neural Networks, we develop a model able to…
Continuous Methods : Hamiltonian Domain Translation
Emmanuel Menier, Michele Alessandro Bucci, Mouadh Yagoubi +2
This paper proposes a novel approach to domain translation. Leveraging established parallels between generative models and dynamical systems, we propose a reformulation of the Cycl…
CD-ROM: Complemented Deep-Reduced Order Model
Emmanuel Menier, Michele Alessandro Bucci, Mouadh Yagoubi +2
Model order reduction through the POD-Galerkin method can lead to dramatic gains in terms of computational efficiency in solving physical problems. However, the applicability of th…
Curriculum learning for data-driven modeling of dynamical systems
Alessandro Bucci, Onofrio Semeraro, Alexandre Allauzen +2
The reliable prediction of the temporal behavior of complex systems is key in numerous scientific fields. This strong interest is however hindered by modeling issues: often, the go…
Control of chaotic systems by Deep Reinforcement Learning
Michele Alessandro Bucci, Onofrio Semeraro, Alexandre Allauzen +3
Deep Reinforcement Learning (DRL) is applied to control a nonlinear, chaotic system governed by the one-dimensional Kuramoto-Sivashinsky (KS) equation. DRL uses reinforcement learn…
Time-stepping and Krylov methods for large-scale instability problems
Jean-Christophe Loiseau, Michele Alessandro Bucci, Stefania Cherubini +1
With the ever increasing computational power available and the development of high-performances computing, investigating the properties of realistic very large-scale nonlinear dyna…