arXiv Machine Learning August 19, 2026 By Jaemin Oh, Jinsil Lee, Youngjoon Hong

Neural Operator-Based Nonlinear Nudging for Chaotic Dynamical Systems

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The paper introduces neural network nudging, a data‑driven method for learning observation‑driven control terms in nonlinear state‑space models. It builds on Kazantzis–Kravaris–Luenberger observer theory to prove the existence of such terms and demonstrates the approach on three chaotic benchmarks: Lorenz 96, Kuramoto–Sivashinsky, and Kolmogorov flow.

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