99 citations · 702 across the 46 of their papers we have counts for
14 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…
Koopman Autoencoders Learn Neural Representation Dynamics
Nishant Suresh Aswani, Saif Eddin Jabari
This paper explores a simple question: can we model the internal transformations of a neural network using dynamical systems theory? We introduce Koopman autoencoders to capture ho…
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…
FlowMixer: A Depth-Agnostic Neural Architecture for Interpretable Spatiotemporal Forecasting
Fares B. Mehouachi, Saif Eddin Jabari
We introduce FlowMixer, a single-layer neural architecture that leverages constrained matrix operations to model structured spatiotemporal patterns with enhanced interpretability.…
Catastrophic Overfitting, Entropy Gap and Participation Ratio: A Noiseless Norm Solution for Fast Adversarial Training
Fares B. Mehouachi, Saif Eddin Jabari
Adversarial training is a cornerstone of robust deep learning, but fast methods like the Fast Gradient Sign Method (FGSM) often suffer from Catastrophic Overfitting (CO), where mod…
Representing Neural Network Layers as Linear Operations via Koopman Operator Theory
Nishant Suresh Aswani, Saif Eddin Jabari, Muhammad Shafique
The strong performance of simple neural networks is often attributed to their nonlinear activations. However, a linear view of neural networks makes understanding and controlling n…
Fourier neural operator for learning solutions to macroscopic traffic flow models: Application to the forward and inverse problems
Bilal Thonnam Thodi, Sai Venkata Ramana Ambadipudi, Saif Eddin Jabari
Deep learning methods are emerging as popular computational tools for solving forward and inverse problems in traffic flow. In this paper, we study a neural operator framework for…
Optimal Smoothing Distribution Exploration for Backdoor Neutralization in Deep Learning-based Traffic Systems
Yue Wang, Wending Li, Michail Maniatakos +1
Deep Reinforcement Learning (DRL) enhances the efficiency of Autonomous Vehicles (AV), but also makes them susceptible to backdoor attacks that can result in traffic congestion or…
Learning-based solutions to nonlinear hyperbolic PDEs: Empirical insights on generalization errors
Bilal Thonnam Thodi, Sai Venkata Ramana Ambadipudi, Saif Eddin Jabari
We study learning weak solutions to nonlinear hyperbolic partial differential equations (H-PDE), which have been difficult to learn due to discontinuities in their solutions. We us…
PiDAn: A Coherence Optimization Approach for Backdoor Attack Detection and Mitigation in Deep Neural Networks
Yue Wang, Wenqing Li, Esha Sarkar +3
Backdoor attacks impose a new threat in Deep Neural Networks (DNNs), where a backdoor is inserted into the neural network by poisoning the training dataset, misclassifying inputs t…
Learning Traffic Speed Dynamics from Visualizations
Bilal Thonnam Thodi, Zaid Saeed Khan, Saif Eddin Jabari +1
Space-time visualizations of macroscopic or microscopic traffic variables is a qualitative tool used by traffic engineers to understand and analyze different aspects of road traffi…
Incorporating Kinematic Wave Theory into a Deep Learning Method for High-Resolution Traffic Speed Estimation
Bilal Thonnam Thodi, Zaid Saeed Khan, Saif Eddin Jabari +1
We propose a kinematic wave-based Deep Convolutional Neural Network (Deep CNN) to estimate high-resolution traffic speed fields from sparse probe vehicle trajectories. We introduce…