Full Domain Analysis in Fluid Dynamics
- Novel techniques in evolutionary optimization, simulation, and machine learning enable a broad analysis of domains like fluid dynamics, in which computation is expensive and flow behavior is complex. This paper introduces the concept of full domain analysis, defined as the ability to efficiently determine the full space of solutions in a problem domain and analyze the behavior of those solutions in an accessible and interactive manner. The goal of full domain analysis is to deepen our understanding of domains by generating many examples of flow, their diversification, optimization, and analysis. We define a formal model for full domain analysis, its current state of the art, and the requirements of its sub-components. Finally, an example is given to show what can be learned by using full domain analysis. Full domain analysis, rooted in optimization and machine learning, can be a valuable tool in understanding complex systems in computational physics and beyond.
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| Document Type: | Article |
|---|---|
| Language: | English |
| Author: | Alexander Hagg, Adam Gaier, Dominik Wilde, Alexander Asteroth, Holger Foysi, Dirk Reith |
| Parent Title (English): | Machine Learning and Knowledge Extraction |
| Volume: | 7 |
| Issue: | 3 |
| Article Number: | 86 |
| Number of pages: | 27 |
| ISSN: | 2504-4990 |
| URN: | urn:nbn:de:hbz:1044-opus-91588 |
| DOI: | https://doi.org/10.3390/make7030086 |
| Publisher: | MDPI |
| Place of publication: | Basel |
| Publishing Institution: | Hochschule Bonn-Rhein-Sieg |
| Date of first publication: | 2025/08/18 |
| Copyright: | © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license |
| Funding: | This research was funded by the German Federal Ministry of Education and Research (BMBF) grant number 03FH012PX5. The computer hardware was supported by the Federal Ministry for Education and Research and by the Ministry for Innovation, Science, Research, and Technology of the state of North Rhine-Westphalia, grant number 13FH156IN6. |
| Tag: | Surrogate models; domain analysis; encodings; generative artificial intelligence; quality–diversity optimization |
| Departments, institutes and facilities: | Fachbereich Informatik |
| Fachbereich Ingenieurwissenschaften und Kommunikation | |
| Institut für Technik, Ressourcenschonung und Energieeffizienz (TREE) | |
| Projects: | AErOMAt - Automatisiertes Entwickeln aerodynamischer Strukturen und Fahrzeuge mithilfe evolutionärer Optimierung und Surrogatmodellierung (DE/BMBF/03FH012PX5,13FH012PX5) |
| EI-HPC - Enabling Infrastructure for HPC-Applications (DE/BMBF/13FH156IN6) | |
| Dewey Decimal Classification (DDC): | 0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 006 Spezielle Computerverfahren |
| Open access funding: | Hochschule Bonn-Rhein-Sieg / Publikationsfonds / Förderung durch den Publikationsfonds der H-BRS |
| Deutsche Forschungsgemeinschaft / DFG Förderung Open Access Publikationskosten 2023 - 2025 | |
| Entry in this database: | 2025/08/27 |
| Licence (German): |  Creative Commons - CC BY - Namensnennung 4.0 International |
