Generative Design of a Gas Turbine Combustor Using Invertible Neural Networks

Computer Science > Artificial Intelligence arXiv:2604.24322 (cs) [Submitted on 27 Apr 2026] Title:Generative Design of a Gas Turbine Combustor Using Invertible Neural Networks Authors:Patrick Krüger, Hanno Gottschalk, Werner Krebs, Bastian Werdelmann View a PDF of the paper titled Generative Design of a Gas Turbine Combustor Using Invertible Neural Networks, by Patrick Kr\"uger and 3 other authors View PDF HTML (experimental) Abstract:The need to burn 100% H2 in high efficient gas turbines featuring low NOx combustion in premix mode require the complete redesign of the combustion system to ensure stable operation without any flashback. Since all engine frames featuring a power range from 4 MW up to 600 MW are affected, a huge design effort is expected. To reduce this effort, especially to transfer knowledge between the different engine classes, generative design methods using latest AI technology will provide promising potential. In this work, this challenge is approached utilizing the current advances in generative artificial intelligence. We train an Invertible Neural Network (INN) on an expandable database of geometrically parameterized combustor designs with simulated performance labels. Utilizing the INN in its inverse direction, multiple design proposals are generated which fulfill specified performance labels. Subjects: Artificial Intelligence (cs.AI) MSC classes: 68T07 Cite as: arXiv:2604.24322 [cs.AI] (or arXiv:2604.24322v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2604.24322 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Journal reference: Journal of Engineering for Gas Turbines and Power, Jan. 2025, 147(1): 011007 (13 pages) Related DOI: https://doi.org/10.1115/1.4066294 Focus to learn more DOI(s) linking to related resources Submission history From: Patrick Krüger [view email] [v1] Mon, 27 Apr 2026 11:14:06 UTC (1,602 KB) Full-text links: Access Paper: View a PDF of the paper titled Generative Design of a Gas Turbine Combustor Using Invertible Neural Networks, by Patrick Kr\"uger and 3 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with…

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