9 citations · 34 across the 15 of their papers we have counts for
16 papers
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…
Evaluation Methodology for Large Language Models for Multilingual Document Question and Answer
Adar Kahana, Jaya Susan Mathew, Said Bleik +2
With the widespread adoption of Large Language Models (LLMs), in this paper we investigate the multilingual capability of these models. Our preliminary results show that, translati…
Analysis of biologically plausible neuron models for regression with spiking neural networks
Mario De Florio, Adar Kahana, George Em Karniadakis
This paper explores the impact of biologically plausible neuron models on the performance of Spiking Neural Networks (SNNs) for regression tasks. While SNNs are widely recognized f…
Rethinking Skip Connections in Spiking Neural Networks with Time-To-First-Spike Coding
Youngeun Kim, Adar Kahana, Ruokai Yin +4
Time-To-First-Spike (TTFS) coding in Spiking Neural Networks (SNNs) offers significant advantages in terms of energy efficiency, closely mimicking the behavior of biological neuron…
Artificial to Spiking Neural Networks Conversion for Scientific Machine Learning
Qian Zhang, Chenxi Wu, Adar Kahana +4
We introduce a method to convert Physics-Informed Neural Networks (PINNs), commonly used in scientific machine learning, to Spiking Neural Networks (SNNs), which are expected to ha…
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…
Understanding the Efficacy of U-Net & Vision Transformer for Groundwater Numerical Modelling
Maria Luisa Taccari, Oded Ovadia, He Wang +3
This paper presents a comprehensive comparison of various machine learning models, namely U-Net, U-Net integrated with Vision Transformers (ViT), and Fourier Neural Operator (FNO),…
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…
ViTO: Vision Transformer-Operator
Oded Ovadia, Adar Kahana, Panos Stinis +2
We combine vision transformers with operator learning to solve diverse inverse problems described by partial differential equations (PDEs). Our approach, named ViTO, combines a U-N…
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…
SMS: Spiking Marching Scheme for Efficient Long Time Integration of Differential Equations
Qian Zhang, Adar Kahana, George Em Karniadakis +1
We propose a Spiking Neural Network (SNN)-based explicit numerical scheme for long time integration of time-dependent Ordinary and Partial Differential Equations (ODEs, PDEs). The…
On the Geometry Transferability of the Hybrid Iterative Numerical Solver for Differential Equations
Adar Kahana, Enrui Zhang, Somdatta Goswami +3
The discovery of fast numerical solvers prompted a clear and rapid shift towards iterative techniques in many applications, especially in computational mechanics, due to the increa…
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,…
Blending Neural Operators and Relaxation Methods in PDE Numerical Solvers
Enrui Zhang, Adar Kahana, Alena Kopaničáková +4
Neural networks suffer from spectral bias having difficulty in representing the high frequency components of a function while relaxation methods can resolve high frequencies effici…
A Convolutional Dispersion Relation Preserving Scheme for the Acoustic Wave Equation
Oded Ovadia, Adar Kahana, Eli Turkel
We propose an accurate numerical scheme for approximating the solution of the two dimensional acoustic wave problem. We use machine learning to find a stencil suitable even in the…
Spiking Neural Operators for Scientific Machine Learning
Adar Kahana, Qian Zhang, Leonard Gleyzer +1
The main computational task of Scientific Machine Learning (SciML) is function regression, required both for inputs as well as outputs of a simulation. Physics-Informed Neural Netw…