AI RESEARCH
AC-SINDy: Compositional Sparse Identification of Nonlinear Dynamics
arXiv CS.LG
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ArXi:2604.18889v1 Announce Type: new We present AC-SINDy, a compositional extension of the Sparse Identification of Nonlinear Dynamics (SINDy) framework that replaces explicit feature libraries with a structured representation based on arithmetic circuits. Rather than enumerating candidate basis functions, the proposed approach constructs nonlinear features through compositions of linear functions and multiplicative interactions, yielding a compact and scalable parameterization and enabling sparsity to be enforced directly over the computational graph. We also.