dielecnet

DielecNet: AI for Broadband Dielectric Characterization of Dispersive Lossy Materials

https://github.com/nuwansribandara/dielecnet

Science Score: 44.0%

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CITATION.cff file
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Scientific vocabulary similarity
Low similarity (1.7%) to scientific vocabulary

Last synced: 6 months ago · JSON representation ·

Repository

DielecNet: AI for Broadband Dielectric Characterization of Dispersive Lossy Materials

Basic Info

Host: GitHub
Owner: NuwanSriBandara
License: mit
Language: Jupyter Notebook
Default Branch: main
Homepage: https://nuwansribandara.github.io/dielecnet
Size: 8.17 MB

Statistics

Stars: 0
Watchers: 2
Forks: 0
Open Issues: 0
Releases: 0

Created over 2 years ago · Last pushed about 1 year ago

Metadata Files

Readme License Citation

README.md

DielecNet

Title: AI for Broadband Dielectric Characterization of Dispersive Lossy Materials

Project Page

News

(Oct 25, 2024)
- Our paper: "Complex-Valued DNN for Broadband Dielectric Characterization of Dispersive Lossy Materials", is accepted at LACAP 2024. Paper, Code

Owner

Name: Nuwan Sriyantha Bandara
Login: NuwanSriBandara
Kind: user
Location: Singapore
Company: School of Computing and Information Systems, Singapore Management University

Website: https://www.nuwanbandara.com/
Repositories: 16
Profile: https://github.com/NuwanSriBandara

Biomedical Engineer | Enthusiast in AI for Healthcare | Cosmological aficionado

Citation (CITATION.cff)

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: >-
  Complex-Valued DNN for Broadband Dielectric
  Characterization of Dispersive Lossy Materials
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Nuwan
    family-names: Bandara
    affiliation: Singapore Management University
  - given-names: Martina
    family-names: Gugliermino
    affiliation: ' Politecnico di Torino, Italy'
  - given-names: David
    family-names: Rodriguez-Duarte
    affiliation: 'Politecnico di Torino, Italy'
repository-code: 'https://github.com/NuwanSriBandara/DielecNet'
abstract: >-
  This paper presents a broadband dielectric
  characterization method based on a Complex-Valued Deep
  Neural Network (CVNN) that allows the retrieval of
  permittivity and conductivity dispersive lossy materials
  using ad-hoc setups. To validate the method, we
  numerically tested it employing a partially filled
  custom-made double-ridge waveguide setup, working from
  0.95 to 4.2 GHz. Then, to investigate the rationale behind
  CVNN predictions, we utilize model-agnostic explainable-AI
  (XAI) techniques to find the causality with its physics.
  The results demonstrate the flexibility and retrieval
  capabilities of the method, as well as the advantages and
  drawbacks in comparison with traditional techniques.
  Moreover, we publicly release the dataset and codes to
  support further research.
license: MIT

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