Passive Lifted FIR Filters for Nonlinear System Identificationtranslating…

arXiv:2508.05279v2 Announce Type: replace Abstract: Passivity is a fundamental property of physical systems. In data-driven modeling, ensuring that a learned model preserves this structural property is critical to avoiding instability in close loop. Although linear passive system identification is well-established, nonlinear extensions remain challenging. We propose nonlinear operators defined through passivity-preserving lifting of linear passive FIR filters. Passivity is enforced efficiently