5 citations · 11 across the 4 of their papers we have counts for
7 papers · 1 filter
LyAm: Robust Non-Convex Optimization for Stable Learning in Noisy Environments
Elmira Mirzabeigi, Sepehr Rezaee, Kourosh Parand
Training deep neural networks, particularly in computer vision tasks, often suffers from noisy gradients and unstable convergence, which hinder performance and generalization. In t…
Advanced Physics-Informed Neural Network with Residuals for Solving Complex Integral Equations
Mahdi Movahedian Moghaddam, Kourosh Parand, Saeed Reza Kheradpisheh
In this paper, we present the Residual Integral Solver Network (RISN), a novel neural network architecture designed to solve a wide range of integral and integro-differential equat…
PINNIES: An Efficient Physics-Informed Neural Network Framework to Integral Operator Problems
Alireza Afzal Aghaei, Mahdi Movahedian Moghaddam, Kourosh Parand
This paper introduces an efficient tensor-vector product technique for the rapid and accurate approximation of integral operators within physics-informed deep learning frameworks.…
Accelerating Fractional PINNs using Operational Matrices of Derivative
Tayebeh Taheri, Alireza Afzal Aghaei, Kourosh Parand
This paper presents a novel operational matrix method to accelerate the training of fractional Physics-Informed Neural Networks (fPINNs). Our approach involves a non-uniform discre…
deepFDEnet: A Novel Neural Network Architecture for Solving Fractional Differential Equations
Ali Nosrati Firoozsalari, Hassan Dana Mazraeh, Alireza Afzal Aghaei +1
The primary goal of this research is to propose a novel architecture for a deep neural network that can solve fractional differential equations accurately. A Gaussian integration r…
Solving Falkner-Skan type equations via Legendre and Chebyshev Neural Blocks
Alireza Afzal Aghaei, Kourosh Parand, Ali Nikkhah +1
In this paper, a new deep-learning architecture for solving the non-linear Falkner-Skan equation is proposed. Using Legendre and Chebyshev neural blocks, this approach shows how or…