mangaboba / gefest

Toolbox for the generative design of geometrically-encoded physical objects using numerical modelling and evolutionary optimization

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GEFEST (Generative Evolution For Encoded STructures) is a toolbox for the generative design of physical objects.

In core it uses: 1. Numerical modelling to simulate the interaction between object and environment 2. Evolutionary optimization to produce new variants of geometrically-encoded structures

The basic abstractions in GEFEST are Point, Polygon, Structure and Domain. Architecture of the GEFEST can be described as:

The evolutionary workflow of the generative design is the following:

The dynamics of the optimisation can be visualized as (breakwaters optimisation case):

How to use

All details about first steps with GEFEST might be found in the quick start guide and in the tutorial for novices

Project Structure

The latest stable release of GEFEST is on the main branch.

The repository includes the following directories:

• Package core contains the main classes and scripts. It is the core of GEFEST framework;
• Package cases includes several how-to-use-cases where you can start to discover how GEFEST works;
• All unit and integration tests can be observed in the test directory;
• The sources of the documentation are in the docs.

Cases and examples

• Experiments with various real and synthetic cases
• Case devoted to the red blood cell traps design.

Current R&D and future plans

Currently, we are working on integration of new types of physical objects with consideration of their internal structure.n

The major ongoing tasks:

• to make the use of GEFEST more accessible and simple for users
• to integrate three dimensional physical objects
• to implement gradient based approaches for optimization of physical objects
• to improve efficiency of GEFEST's standard sampler

Documentation

Detailed information and description of GEFEST framework is available in the Read the Docs

Contribution guide

The contribution guide is available in the page

Acknowledgments

We acknowledge the contributors for their important impact and the participants of the numerous scientific conferences and workshops for their valuable advice and suggestions.

Contacts

• Telegram channel for solving problems and answering questions on GEFEST
• Natural System Simulation Team
• Newsfeed
• Youtube channel

Supported by

National Center for Cognitive Research of ITMO University

Citation

@article{starodubcev2023generative,

title={Generative design of physical objects using modular framework}, author={Starodubcev, Nikita O and Nikitin, Nikolay O and Andronova, Elizaveta A and Gavaza, Konstantin G and Sidorenko, Denis O and Kalyuzhnaya, Anna V}, journal={Engineering Applications of Artificial Intelligence}, volume={119}, pages={105715}, year={2023}, publisher={Elsevier}

}