A framework identifies homogeneous polynomial dynamical systems from data by directly learning low-rank tensor factors via alternating least-squares on tensor train, hierarchical Tucker, and canonical polyadic decompositions.
Higher-order interactions stabilize dynamics in competitive network models
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Data-Driven Tensor Decomposition Identification of Homogeneous Polynomial Dynamical Systems
A framework identifies homogeneous polynomial dynamical systems from data by directly learning low-rank tensor factors via alternating least-squares on tensor train, hierarchical Tucker, and canonical polyadic decompositions.