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 electrical and structural elements and components. I combine numerical simulation like finite element analysis with artificial intelligence like reinforcement learning to produce optimal designs. Recently, my work has been focused on topology optimization of electrical circuits and mechanical structures. 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 two moduli: topology optimization and offshore pipelay dynamics. 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 and electrical components and elements.
Philosophy of the software:
- free
- open source
- runnable on personal computer
Please check my blog and manuals for the specifics of the models and algorithms I use:
- Engineering Design by Reinforcement Learning and Finite Element Methods.
- Topology optimization with reinforcement learning.
- Using Hierarchical Reinforcement Learning for Fast Topology Optimisation.
- Some philosophical aspects of artificial intelligence.
- Engineering Design by Genetic Algorithms and Finite Element Methods.
- On artificial intelligence aided engineering design.
- Some ideas on using reinforcement learning in marine construction and sustainable energy development.
- Modelling offshore pipelaying dynamics.
- Modelling offshore pipelaying dynamics - Part 2.
- Pipelay vessel design optimisation using genetic algorithms.
- Python and Finite Element Methods: A Match Made in Heaven?
Design of bionic partition (GA on the left, and RL on the right):
Topology optimization by reinforcement learning:
To keep up to date with the project please check Gigala page.