Package: gefest
testsdocslicensesupportgitlabfundingGEFEST(GenerativeEvolutionForEncodedSTructures) 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 structuresThe 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 useAll details about first steps with GEFEST might be found in thequick start guide.Tutorals for more spicific use cases can be foundtutorial section of docs.Project StructureThe latest stable release of GEFEST is on themain branch.The repository includes the following directories:Packagecorecontains the main classes and scripts. It is thecoreof GEFEST framework;Packagecasesincludes severalhow-to-use-caseswhere you can start to discover how GEFEST works;Allunit and integration testscan be observed in thetestdirectory;The sources of the documentation are in thedocs.Weights of pretrained DL models can be downloaded fromthis repository.Cases and examplesNote: To run the examples below, the old kernel gefest version, which can be installed on python 3.7 with:pipinstallgit+https://github.com/aimclub/GEFEST.git@4f9c34c449c0eb65d264476e5145f09b4839cd70Experimentswith various real and synthetic casesCasedevoted to the red blood cell traps design.Migrated examples can be found in cases folder of the main branch.Current R&D and future plansCurrently, we are working on integration of new types of physical objects with consideration of their internal structure.nThe major ongoing tasks:to integrate three dimensional physical objectsto implement gradient based approaches for optimization of physical objectsDocumentationDetailed information and description of GEFEST framework is available in theRead the DocsContribution guideThe contribution guide is available in thepageAcknowledgmentsWe acknowledge the contributors for their important impact and the participants of the numerous scientific conferences
and workshops for their valuable advice and suggestions.ContactsTelegram channel for solving problems and answering questions on GEFESTNatural System Simulation TeamNewsfeedYoutube channelSupported byNational Center for Cognitive Research of ITMO UniversityCitation@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}}