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Open access
Date
2024-04-12Type
- Journal Article
ETH Bibliography
yes
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Abstract
Interacting systems are ubiquitous in nature and engineering, ranging from particle dynamics in physics to functionally connected brain regions. Revealing interaction laws is of fundamental importance but also particularly challenging due to underlying configurational complexities. These challenges become exacerbated for heterogeneous systems that are prevalent in reality, where multiple interaction types coexist simultaneously and relational inference is required. Here, we propose a probabilistic method for relational inference, which possesses two distinctive characteristics compared to existing methods. First, it infers the interaction types of different edges collectively by explicitly encoding the correlation among incoming interactions with a joint distribution, and second, it allows handling systems with variable topological structure over time. We evaluate the proposed methodology across several benchmark datasets and demonstrate that it outperforms existing methods in accurately inferring interaction types. The developed methodology constitutes a key element for understanding interacting systems and may find application in graph structure learning. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000669647Publication status
publishedExternal links
Journal / series
Nature CommunicationsVolume
Pages / Article No.
Publisher
NatureOrganisational unit
09650 - Kammer, David / Kammer, David
Funding
176878 - Data-Driven Intelligent Predictive Maintenance of Industrial Assets (SNF)
ETH-12 21-1 - Physics-induced neural networks for micro-mechanical multi-scale property discovery and prediction (ETHZ)
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ETH Bibliography
yes
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