A Compensated Koopman Neural Operator with Selective State-Space Dynamics for Unsteady Flows

Tangying Lv; Yuanjun Dai; Zhenxu Sun

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arXiv · 2608.25879

A Compensated Koopman Neural Operator with Selective State-Space Dynamics for Unsteady Flows

Tangying Lv·Yuanjun Dai·Zhenxu Sun

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Abstract

Stable prediction of unsteady flows requires accurate multiscale spatial representation and robust temporal propagation. We introduce the Compensated Koopman U-shaped Neural Operator (CoKo-UNO), which combines a U-shaped spectral backbone with Koopman-dominated latent propagation. Finite-dimensional Koopman truncation produces a state-dependent residual that is repeatedly reinjected during autoregressive rollout. CoKo-UNO models this residual with a selective state-space model (SSM), a principled input-dependent compensation mechanism, together with resolution-adaptive compensatory skip connections and an overlapping-warmup rollout strategy. \NEW{Across four benchmark problems, CoKo-UNO achieves the lowest mean rollout error among all compared methods. Its largest gain is a $76.76\%$ reduction relative to the strongest baseline, while requiring about $41.40\%$ of RNO's training time.} These results show that explicit residual compensation improves stable autoregressive prediction of unsteady flows.

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Tangying Lv, Yuanjun Dai, Zhenxu Sun. 2026-08-26. A Compensated Koopman Neural Operator with Selective State-Space Dynamics for Unsteady Flows. https://arxiv.org/abs/2608.25879

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