Add to ORKGInternational audienceThis 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 process unstructured grids with the advantage of enforcing boundary conditions by design. By directly minimizing the residual of the Poisson equation, the model attempts to learn the physics of the problem without the need for exact solutions, in contrast to most previous data-driven processes where the distance with the available solutions is minimized
This paper presents a strategy to accelerate virtually any Poisson solver by taking advantage of s s...
International audienceThe ubiquity of fluids in the physical world explains the need to accurately s...
We propose a novel deep learning (DL) approach to solve one-dimensional non-linear elliptic, parabol...
This paper proposes a novel Machine Learning-based approach to solve a Poisson problem with mixed bo...
International audienceThis paper proposes a novel Machine Learning-based approach to solve a Poisson...
Abstract The Poisson equation is commonly encountered in engineering, for instance, in computational...
This paper presents Ψ-GNN, a novel Graph Neural Network (GNN) approach for solving the ubiquitous Po...
The volume of fluid (VoF) method is widely used in multi-phase flow simulations to track and locate ...
It is the tradition for the fluid community to study fluid dynamics problems via numerical simulatio...
The approach of using physics-based machine learning to solve PDEs has recently become very popular....
This paper introduces a novel two-stream deep model based on graph convolutional network (GCN) archi...
In recent years, the development of deep learning is noticeably influencing the progress of computat...
Physics-Informed Neural Networks (PINN) are neural networks encoding the problem governing equations...
We present a novel physics-informed deep learning framework for solving steady-state incompressible ...
Computational fluid dynamics (CFD) is the de-facto method for solving the Navier-Stokes equations, t...
This paper presents a strategy to accelerate virtually any Poisson solver by taking advantage of s s...
International audienceThe ubiquity of fluids in the physical world explains the need to accurately s...
We propose a novel deep learning (DL) approach to solve one-dimensional non-linear elliptic, parabol...
This paper proposes a novel Machine Learning-based approach to solve a Poisson problem with mixed bo...
International audienceThis paper proposes a novel Machine Learning-based approach to solve a Poisson...
Abstract The Poisson equation is commonly encountered in engineering, for instance, in computational...
This paper presents Ψ-GNN, a novel Graph Neural Network (GNN) approach for solving the ubiquitous Po...
The volume of fluid (VoF) method is widely used in multi-phase flow simulations to track and locate ...
It is the tradition for the fluid community to study fluid dynamics problems via numerical simulatio...
The approach of using physics-based machine learning to solve PDEs has recently become very popular....
This paper introduces a novel two-stream deep model based on graph convolutional network (GCN) archi...
In recent years, the development of deep learning is noticeably influencing the progress of computat...
Physics-Informed Neural Networks (PINN) are neural networks encoding the problem governing equations...
We present a novel physics-informed deep learning framework for solving steady-state incompressible ...
Computational fluid dynamics (CFD) is the de-facto method for solving the Navier-Stokes equations, t...
This paper presents a strategy to accelerate virtually any Poisson solver by taking advantage of s s...
International audienceThe ubiquity of fluids in the physical world explains the need to accurately s...
We propose a novel deep learning (DL) approach to solve one-dimensional non-linear elliptic, parabol...