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Learning interacting particle systems: Diffusion parameter estimation for aggregation equations
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DOI：10.1142/S0218202519500015.png)
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Abstract
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In this paper, we study the parameter estimation of interacting particle systems subject to the Newtonian aggregation and Brownian diffusion. Specifically, we construct an estimator v with partial observed data to approximate the diffusion parameter (v) over cap and the estimation error is achieved. Furthermore, we extend this result to general aggregation equations with a bounded Lipschitz interaction field.
Keywords:
Inverse problem
parameter identification of agent-based model
mean-field limit
data assimilation
concentration inequality
discrete observation
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