2 citations · 5 across the 8 of their papers we have counts for
9 papers
MINPO: Memory-Informed Neural Pseudo-Operator to Resolve Nonlocal Spatiotemporal Dynamics
Farinaz Mostajeran, Aruzhan Tleubek, Salah A Faroughi
Many physical systems exhibit nonlocal spatiotemporal behaviors described by integro-differential equations (IDEs). Classical methods for solving IDEs require repeatedly evaluating…
Scientific Machine Learning with Kolmogorov-Arnold Networks
Salah A. Faroughi, Farinaz Mostajeran, Amin Hamed Mashhadzadeh +1
The field of scientific machine learning, which originally utilized multilayer perceptrons (MLPs), is increasingly adopting Kolmogorov-Arnold Networks (KANs) for data encoding. Thi…
Neural Tangent Kernel Analysis to Probe Convergence in Physics-informed Neural Solvers: PIKANs vs. PINNs
Salah A. Faroughi, Farinaz Mostajeran
Physics-informed Kolmogorov-Arnold Networks (PIKANs), and in particular their Chebyshev-based variants (cPIKANs), have recently emerged as promising models for solving partial diff…
Frequency-Aware Vision Transformers for High-Fidelity Super-Resolution of Earth System Models
Ehsan Zeraatkar, Salah A Faroughi, Jelena Tešić
Super-resolution can play an essential role in enhancing the spatial fidelity of Earth System Model outputs, allowing fine-scale structures highly beneficial to climate science to…
ViSIR: Vision Transformer Single Image Reconstruction Method for Earth System Models
Ehsan Zeraatkar, Salah Faroughi, Jelena Tešić
Purpose: Earth system models (ESMs) integrate the interactions of the atmosphere, ocean, land, ice, and biosphere to estimate the state of regional and global climate under a wide…
Scaled-cPIKANs: Domain Scaling in Chebyshev-based Physics-informed Kolmogorov-Arnold Networks
Farinaz Mostajeran, Salah A Faroughi
Partial Differential Equations (PDEs) are integral to modeling many scientific and engineering problems. Physics-informed Neural Networks (PINNs) have emerged as promising tools fo…
EPi-cKANs: Elasto-Plasticity Informed Kolmogorov-Arnold Networks Using Chebyshev Polynomials
Farinaz Mostajeran, Salah A Faroughi
Multilayer perceptron (MLP) networks are predominantly used to develop data-driven constitutive models for granular materials. They offer a compelling alternative to traditional ph…
Particle-level Simulation of Magnetorheological Fluids: A Fully-Resolved Solver
C. Fernandes, Salah A. Faroughi
Magnetorheological fluids (MRFs) are smart materials consisting of micro-scale magnetizable particles suspended in a carrier fluid. The rheological properties of a MRF can be chang…
Finite volume simulations of particle-laden viscoelastic fluid flows: application to hydraulic fracture processes
Célio Fernandes, Salah Faroughi, Ricardo Ribeiro +2
Accurately resolving the coupled momentum transfer between the liquid and solid phases of complex fluids is a fundamental problem in multiphase transport processes, such as hydraul…