project
Machine Learning methods for solving differential equations of nonlinear optical systems
Science Score: 44.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
-
○Academic publication links
-
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (1.1%) to scientific vocabulary
Last synced: 10 months ago
·
JSON representation
·
Repository
Machine Learning methods for solving differential equations of nonlinear optical systems
Basic Info
- Host: GitHub
- Owner: diogoMealha
- Language: Jupyter Notebook
- Default Branch: main
- Size: 575 KB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Created almost 2 years ago
· Last pushed almost 2 years ago
Metadata Files
Readme
Citation
README.md
Project
Machine Learning methods for solving differential equations of nonlinear optical systems
Owner
- Name: Diogo Mealha
- Login: diogoMealha
- Kind: user
- Repositories: 1
- Profile: https://github.com/diogoMealha
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: ODESolver
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Diogo Alves
family-names: Mealha
email: diogo@mealha.com
affiliation: University of Aveiro
repository-code: 'https://github.com/diogoMealha/Project'
abstract: >-
This study explored the use of neural networks to solve
ordinary differential equations, leveraging the Universal
Approximation Theorem. Using a trial solution method,
neural networks were employed to approximate solutions to
differential equations by directly incorporating the
equations into the cost function. This approach bypasses
the need for training data, as the network learns
solutions that naturally satisfy the equation to be
solved. Finally, this methodology was applied to ordinary
differential equations in nonlinear optics, such as those
describing soliton-type solutions of the nonlinear
Schrödinger equation and the complex Ginzburg-Landau
equation, where different models were tested and compared.
The results highlight the potential of neural networks to
accurately solve nonlinear boundary value problems.
keywords:
- machine learing
- neural networks
- ordinary differential equations
- optical systems
license: MIT
version: '1.0'