Distributed Koopman Operator Learning from Sequential Observations
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911602260836352 |
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| author | Azarbahram, Ali Liu, Shenyu Incremona, Gian Paolo |
| author_facet | Azarbahram, Ali Liu, Shenyu Incremona, Gian Paolo |
| contents | This paper presents a distributed Koopman operator learning framework for modeling unknown nonlinear dynamics using sequential observations from multiple agents. Each agent estimates a local Koopman approximation based on lifted data and collaborates over a communication graph to reach exponential consensus on a consistent distributed approximation. The approach supports distributed computation under asynchronous and resource-constrained sensing. Its performance is demonstrated through simulation results, validating convergence and predictive accuracy under sensing-constrained scenarios and limited communication. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_20071 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Distributed Koopman Operator Learning from Sequential Observations Azarbahram, Ali Liu, Shenyu Incremona, Gian Paolo Systems and Control This paper presents a distributed Koopman operator learning framework for modeling unknown nonlinear dynamics using sequential observations from multiple agents. Each agent estimates a local Koopman approximation based on lifted data and collaborates over a communication graph to reach exponential consensus on a consistent distributed approximation. The approach supports distributed computation under asynchronous and resource-constrained sensing. Its performance is demonstrated through simulation results, validating convergence and predictive accuracy under sensing-constrained scenarios and limited communication. |
| title | Distributed Koopman Operator Learning from Sequential Observations |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2509.20071 |