| Abstract: |
| Interacting particle systems provide a powerful modeling framework for collective dynamics in nature and engineering. While prior methods have primarily addressed symmetric interactions using various learning techniques, many real-world systems exhibit asymmetric interactions, which demand more general and flexible modeling tools. In this talk, I will present a new Sparse Bayesian Learning (SBL) framework for identifying asymmetric interaction kernels in the Motsch-Tadmor model. By reformulating the nonlinear inverse problem as a subspace identification task, we establish identifiability guarantees and enable robust kernel recovery. Incorporating informative priors, the proposed SBL algorithm offers principled model selection and uncertainty quantification, achieving reliable inference from noisy trajectory data. |
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