How to use

All details about first steps with GEFEST might be found in the quick start guide.

Tutorals for more spicific use cases can be found tutorial section of docs.

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.

• Weights of pretrained DL models can be downloaded from this repository.

Cases and examples

Note: To run the examples below, the old kernel gefest version, which can be installed on python 3.7 with:

pip install git+https://github.com/aimclub/GEFEST.git@4f9c34c449c0eb65d264476e5145f09b4839cd70

• Experiments with various real and synthetic cases

• Case devoted to the red blood cell traps design.

Migrated examples can be found in cases folder of the main branch.

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 integrate three dimensional physical objects

• to implement gradient based approaches for optimization of physical objects

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}}

@inproceedings{solovev2023ai,

title={AI Framework for Generative Design of Computational Experiments with Structures in Physical Environment}, author={Solovev, Gleb Vitalevich and Kalyuzhnaya, Anna and Hvatov, Alexander and Starodubcev, Nikita and Petrov, Oleg and Nikitin, Nikolay}, booktitle={NeurIPS 2023 AI for Science Workshop}, year={2023}}