AI RESEARCH
Parametric Interpolation of Dynamic Mode Decomposition for Predicting Nonlinear Systems
arXiv CS.LG
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ArXi:2604.12103v1 Announce Type: cross We present parameter-interpolated dynamic mode decomposition (piDMD), a parametric reduced-order modeling framework that embeds known parameter-affine structure directly into the DMD regression step. Unlike existing parametric DMD methods which interpolate modes, eigenvalues, or reduced operators and can be fragile with sparse