9 citations · 34 across the 15 of their papers we have counts for
5 papers · 1 filter
Fast meta-solvers for 3D complex-shape scatterers using neural operators trained on a non-scattering problem
Youngkyu Lee, Shanqing Liu, Zongren Zou +5
Three-dimensional target identification using scattering techniques requires high accuracy solutions and very fast computations for real-time predictions in some critical applicati…
MyCrunchGPT: A chatGPT assisted framework for scientific machine learning
Varun Kumar, Leonard Gleyzer, Adar Kahana +2
Scientific Machine Learning (SciML) has advanced recently across many different areas in computational science and engineering. The objective is to integrate data and physics seaml…
Real-time Inference and Extrapolation via a Diffusion-inspired Temporal Transformer Operator (DiTTO)
Oded Ovadia, Vivek Oommen, Adar Kahana +3
Extrapolation remains a grand challenge in deep neural networks across all application domains. We propose an operator learning method to solve time-dependent partial differential…
MessageNet: Message Classification using Natural Language Processing and Meta-data
Adar Kahana, Oren Elisha
In this paper we propose a new Deep Learning (DL) approach for message classification. Our method is based on the state-of-the-art Natural Language Processing (NLP) building blocks…
A physically-informed Deep-Learning approach for locating sources in a waveguide
Adar Kahana, Symeon Papadimitropoulos, Eli Turkel +1
Inverse source problems are central to many applications in acoustics, geophysics, non-destructive testing, and more. Traditional imaging methods suffer from the resolution limit,…