1 citations · 1 across the 9 of their papers we have counts for
11 papers
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…
Backpressure-based Mean-field Type Game for Scheduling in Multi-Hop Wireless Sensor Networks
Salah Eddine Choutri, Boualem Djehiche, Prajwal Chauhan +1
We propose a Mean-Field Type Game (MFTG) framework for effective scheduling in multi-hop wireless sensor networks (WSNs) using backpressure as a performance criterion. Traditional…
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…
Stability Via Adversarial Training of Neural Network Stochastic Control of Mean-Field Type
Julian Barreiro-Gomez, Salah Eddine Choutri, Boualem Djehiche
In this paper, we present an approach to neural network mean-field-type control and its stochastic stability analysis by means of adversarial inputs (aka adversarial attacks). This…