4 papers · 1 filter
Efficient Dilated Squeeze and Excitation Neural Operator for Differential Equations
Prajwal Chauhan, Salah Eddine Choutri, Saif Eddin Jabari
Fast and accurate surrogates for physics-driven partial differential equations (PDEs) are essential in fields such as aerodynamics, porous media design, and flow control. However,…
Monte Carlo-Type Neural Operator for Differential Equations
Salah Eddine Choutri, Prajwal Chauhan, Othmane Mazhar +1
The Monte Carlo-type Neural Operator (MCNO) introduces a framework for learning solution operators of one-dimensional partial differential equations (PDEs) by directly learning the…
Learning Solution Operators for Partial Differential Equations via Monte Carlo-Type Approximation
Salah Eddine Choutri, Prajwal Chauhan, Othmane Mazhar +1
The Monte Carlo-type Neural Operator (MCNO) introduces a lightweight architecture for learning solution operators for parametric PDEs by directly approximating the kernel integral…
Neural operators struggle to learn complex PDEs in pedestrian mobility: Hughes model case study
Prajwal Chauhan, Salah Eddine Choutri, Mohamed Ghattassi +2
This paper investigates the limitations of neural operators in learning solutions for a Hughes model, a first-order hyperbolic conservation law system for crowd dynamics. The model…