Science Score: 54.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
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✓Academic publication links
Links to: researchgate.net -
○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (11.7%) to scientific vocabulary
Keywords
Repository
Engineering design by reinforcement learning.
Basic Info
Statistics
- Stars: 56
- Watchers: 4
- Forks: 14
- Open Issues: 1
- Releases: 0
Topics
Metadata Files
README.md
Engineering design by reinforcement learning, genetic algorithms and finite element methods
Are you interested in new ways of engineering design? This repository is an attempt to apply artificial intelligence algorithms for the purpose of engineering design of mechanical and structural elements and components. I combine numerical simulation like finite element analysis with artificial intelligence like reinforcement learning to produce optimal designs. Starting from 2018, my work has been focused on topology optimization of mechanical structures and elements. I am constantly exploring different ways that AI can be applied to science and engineering. With my diverse interests, I am using this repository as a testbed for my ideas to create software for artificial intelligence aided design. I hope that my work can inspire you to explore new ways that AI can be applied to your field.
At present, Gigala software consists of topology optimization module, and offshore pipelay dynamics module (now separated into Ocean Intella software). It uses artificial intelligence to assist an engineer in her design. You can use it as research or engineering analysis tool to design different mechanical components and elements.
Philosophy of the software: * free * open source * practical performance on your PC
Please check my Blog and ResearchGate for the specifics of the models and algorithms I use.
For citation please use Reinforcement Learning Guided Engineering Design: from Topology Optimization to Advanced Modelling
Topology optimization by reinforcement learning:
Topology optimization by genetic algorithms:
Pseudo 3D topology optimization by reinforcement learning (work in progress, see preprint ):
To keep up to date with the project please check Gigala page.
If you like my project and want to support it, please consider doing any of the following:
Owner
- Name: Georgy Tskhondiya
- Login: gigatskhondia
- Kind: user
- Location: Tbilisi, Georgia
- Company: Gigala
- Website: https://www.facebook.com/GigaTsk
- Twitter: giorgitskhondi1
- Repositories: 2
- Profile: https://github.com/gigatskhondia
Solving creativity to advance science and engineering. Making AI accessible.
Citation (CITATION.cff)
cff-version: 1.2.0
title: Gigala
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Giorgi
family-names: Tskhondia
email: gigatskhondia@gmail.com
affiliation: Independent researcher
identifiers:
- type: url
value: 'https://gigatskhondia.medium.com/'
description: Medium blog about my project's developments.
- type: url
value: 'https://www.researchgate.net/profile/Giorgi-Tskhondia'
description: My ResearchGate profile.
repository-code: 'https://github.com/gigatskhondia/gigala'
abstract: >-
Applying artificial intelligence algorithms for the
purpose of engineering design.
keywords:
- Reinforcement learning
- Finite element methods
- Structural engineering
- Design
- Topology optimization
license: MIT
date-released: '2018-10-13'
GitHub Events
Total
- Issues event: 1
- Watch event: 5
- Issue comment event: 1
- Push event: 175
- Fork event: 1
Last Year
- Issues event: 1
- Watch event: 5
- Issue comment event: 1
- Push event: 175
- Fork event: 1
Dependencies
- gym *
- pyglet *
- six *
- torch *