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New WSINDy method learns models from network dynamics data

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Researchers have developed a new method called Weak Form Sparse Identification of Nonlinear Dynamics (WSINDy) to learn effective models from network dynamics data. This approach is particularly useful for understanding social systems where individuals influence each other. The study demonstrates that while more data trajectories improve accuracy in noisy conditions, the gains diminish significantly after a small number of additional trajectories. The method can also identify ordinary differential equations directly from stochastic processes, offering better insights than traditional mean-field approximations when they fail. AI

RANK_REASON The cluster contains an academic paper detailing a new method for learning models from data. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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New WSINDy method learns models from network dynamics data

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The cluster contains an academic paper detailing a new method for learning models from data. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Moyi Tian, Daniel A. Messenger, Vanja Dukic, Nancy Rodr\'iguez, David M. Bortz ·

    Learning effective models from network dynamics data with multiple initial conditions using weak form SINDy

    arXiv:2605.30432v1 Announce Type: cross Abstract: Social systems consist of networks of individuals who influence one another through social interactions. Studying how processes evolve on these networks can help us better understand patterns of social behavior. We study a system …

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