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arXiv · 2509.20071

Distributed Koopman Operator Learning from Sequential Observations

Ali Azarbahram·Shenyu Liu·Gian Paolo Incremona
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Abstract

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.

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Ali Azarbahram, Shenyu Liu, Gian Paolo Incremona. 2025-09-24. Distributed Koopman Operator Learning from Sequential Observations. https://doi.org/10.1016/j.ejcon.2026.101497

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