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Found 14 papers, 0 papers with code
Large scale scattering using fast solvers based on neural operators
We extend a recently proposed machine-learning-based iterative solver, i. e. the hybrid iterative transferable solver (HINTS), to solve the scattering problem described by the Helmholtz equation in an exterior domain with a complex absorbing boundary condition.
Evaluation Methodology for Large Language Models for Multilingual Document Question and Answer
With the widespread adoption of Large Language Models (LLMs), in this paper we investigate the multilingual capability of these models.
Rethinking Skip Connections in Spiking Neural Networks with Time-To-First-Spike Coding
In this work, we delve into the role of skip connections, a widely used concept in Artificial Neural Networks (ANNs), within the domain of SNNs with TTFS coding.
Artificial to Spiking Neural Networks Conversion for Scientific Machine Learning
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 have higher energy efficiency compared to traditional Artificial Neural Networks (ANNs).
Real-time Inference and Extrapolation via a Diffusion-inspired Temporal Transformer Operator (DiTTO)
The proposed method, named Diffusion-inspired Temporal Transformer Operator (DiTTO), is inspired by latent diffusion models and their conditioning mechanism, which we use to incorporate the temporal evolution of the PDE, in combination with elements from the transformer architecture to improve its capabilities.
Understanding the Efficacy of U-Net & Vision Transformer for Groundwater Numerical Modelling
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), for time-dependent forward modelling in groundwater systems.
MyCrunchGPT: A chatGPT assisted framework for scientific machine learning
To demonstrate the flow of the MyCrunchGPT, and create an infrastructure that can facilitate a broader vision, we built a webapp based guided user interface, that includes options for a comprehensive summary report.
ViTO: Vision Transformer-Operator
We combine vision transformers with operator learning to solve diverse inverse problems described by partial differential equations (PDEs).
MessageNet: Message Classification using Natural Language Processing and Meta-data
To achieve message representation, each type of input is processed in a dedicated block in the neural network architecture that is suitable for the data type.
SMS: Spiking Marching Scheme for Efficient Long Time Integration of Differential Equations
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).
Blending Neural Operators and Relaxation Methods in PDE Numerical Solvers
Neural networks suffer from spectral bias having difficulty in representing the high frequency components of a function while relaxation methods can resolve high frequencies efficiently but stall at moderate to low frequencies.
A physically-informed Deep-Learning approach for locating sources in a waveguide
Inverse source problems are central to many applications in acoustics, geophysics, non-destructive testing, and more.
A Convolutional Dispersion Relation Preserving Scheme for the Acoustic Wave Equation
We propose an accurate numerical scheme for approximating the solution of the two dimensional acoustic wave problem.
Spiking Neural Operators for Scientific Machine Learning
We demonstrate this new approach for classification using the SNN in the branch, achieving results comparable to the literature.
