Digital twins as self-models for intelligent structures
Abstract A self-model is an artificial intelligence that is able to create a continuously updated internal representation of itself. In this paper we use an agent-based architecture to create a ‘digital twin self-model’, using the example of a small-scale three-story building. The architecture is ba...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-14347-8 |
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| author | Xiaoxue Shen David J. Wagg Matthew Tipuric Matthew S. Bonney |
| author_facet | Xiaoxue Shen David J. Wagg Matthew Tipuric Matthew S. Bonney |
| author_sort | Xiaoxue Shen |
| collection | DOAJ |
| description | Abstract A self-model is an artificial intelligence that is able to create a continuously updated internal representation of itself. In this paper we use an agent-based architecture to create a ‘digital twin self-model’, using the example of a small-scale three-story building. The architecture is based on a set of heterogeneous digital components, each managed by an agent. The agents can be orchestrated to perform a specific workflow, or collaborate with a human user to perform requested tasks. The digital twin architecture enables multiple complex behaviors to be represented via a time-evolving dynamic assembly of the digital components, that also includes the encoding of a self-model in a knowledge graph as well as producing quantitative outputs. Four operational modes are defined for the digital twin and the example shown here demonstrates an offline mode that executes a predefined workflow with five agents. The digital twin has an information management system which is coordinated using a dynamic knowledge graph that encodes the self-model. Users can visualize the knowledge graph via a web-based user interface and also input natural language queries. Retrieval augmented generation is used to give a response to the queries using both the local knowledge graph and a large language model. |
| format | Article |
| id | doaj-art-7b949a59d2904a1aa7a4e5bd87c8e215 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-7b949a59d2904a1aa7a4e5bd87c8e2152025-08-24T11:21:35ZengNature PortfolioScientific Reports2045-23222025-08-0115111110.1038/s41598-025-14347-8Digital twins as self-models for intelligent structuresXiaoxue Shen0David J. Wagg1Matthew Tipuric2Matthew S. Bonney3The Alan Turing InstituteThe Alan Turing InstituteThe Alan Turing InstituteSchool of Aerospace, Civil, Electrical and Mechanical Engineering, Swansea UniversityAbstract A self-model is an artificial intelligence that is able to create a continuously updated internal representation of itself. In this paper we use an agent-based architecture to create a ‘digital twin self-model’, using the example of a small-scale three-story building. The architecture is based on a set of heterogeneous digital components, each managed by an agent. The agents can be orchestrated to perform a specific workflow, or collaborate with a human user to perform requested tasks. The digital twin architecture enables multiple complex behaviors to be represented via a time-evolving dynamic assembly of the digital components, that also includes the encoding of a self-model in a knowledge graph as well as producing quantitative outputs. Four operational modes are defined for the digital twin and the example shown here demonstrates an offline mode that executes a predefined workflow with five agents. The digital twin has an information management system which is coordinated using a dynamic knowledge graph that encodes the self-model. Users can visualize the knowledge graph via a web-based user interface and also input natural language queries. Retrieval augmented generation is used to give a response to the queries using both the local knowledge graph and a large language model.https://doi.org/10.1038/s41598-025-14347-8Digital twinSelf-modelAgentStructure |
| spellingShingle | Xiaoxue Shen David J. Wagg Matthew Tipuric Matthew S. Bonney Digital twins as self-models for intelligent structures Scientific Reports Digital twin Self-model Agent Structure |
| title | Digital twins as self-models for intelligent structures |
| title_full | Digital twins as self-models for intelligent structures |
| title_fullStr | Digital twins as self-models for intelligent structures |
| title_full_unstemmed | Digital twins as self-models for intelligent structures |
| title_short | Digital twins as self-models for intelligent structures |
| title_sort | digital twins as self models for intelligent structures |
| topic | Digital twin Self-model Agent Structure |
| url | https://doi.org/10.1038/s41598-025-14347-8 |
| work_keys_str_mv | AT xiaoxueshen digitaltwinsasselfmodelsforintelligentstructures AT davidjwagg digitaltwinsasselfmodelsforintelligentstructures AT matthewtipuric digitaltwinsasselfmodelsforintelligentstructures AT matthewsbonney digitaltwinsasselfmodelsforintelligentstructures |