160 citations · 166 across the 8 of their papers we have counts for
8 papers
Flow Matching and Diffusion Models via PointNet for Generating Fluid Fields on Irregular Geometries
Ali Kashefi
We present two novel generative geometric deep learning frameworks, termed Flow Matching PointNet and Diffusion PointNet, for predicting fluid flow variables on irregular geometrie…
Vision Mamba for Permeability Prediction of Porous Media
Ali Kashefi, Tapan Mukerji
Vision Mamba has recently received attention as an alternative to Vision Transformers (ViTs) for image classification. The network size of Vision Mamba scales linearly with input i…
Physics-informed KAN PointNet: Deep learning for simultaneous solutions to inverse problems in incompressible flow on numerous irregular geometries
Ali Kashefi, Tapan Mukerji
Kolmogorov-Arnold Networks (KANs) have gained attention as an alternative to traditional multilayer perceptrons (MLPs) for deep learning applications in computational physics, part…
PointNet with KAN versus PointNet with MLP for 3D Classification and Segmentation of Point Sets
Ali Kashefi
Kolmogorov-Arnold Networks (KANs) have recently gained attention as an alternative to traditional Multilayer Perceptrons (MLPs) in deep learning frameworks. KANs have been integrat…
Kolmogorov-Arnold PointNet: Deep learning for prediction of fluid fields on irregular geometries
Ali Kashefi
Kolmogorov-Arnold Networks (KANs) have emerged as a promising alternative to traditional Multilayer Perceptrons (MLPs) in deep learning. KANs have already been integrated into vari…
A Misleading Gallery of Fluid Motion by Generative Artificial Intelligence
Ali Kashefi
In this technical report, we extensively investigate the accuracy of outputs from well-known generative artificial intelligence (AI) applications in response to prompts describing…
A novel Fourier neural operator framework for classification of multi-sized images: Application to three dimensional digital porous media
Ali Kashefi, Tapan Mukerji
Fourier neural operators (FNOs) are invariant with respect to the size of input images, and thus images with any size can be fed into FNO-based frameworks without any modification…
Physics-informed PointNet: A deep learning solver for steady-state incompressible flows and thermal fields on multiple sets of irregular geometries
Ali Kashefi, Tapan Mukerji
We present a novel physics-informed deep learning framework for solving steady-state incompressible flow on multiple sets of irregular geometries by incorporating two main elements…