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ZNO introduces stable rational neural operators for discrete-time dynamics

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced the Z-Domain Neural Operator (ZNO), a novel causal neural operator designed for discrete-time dynamic systems. ZNO's architecture utilizes stable low-rank rational filters parameterized in the z-plane, addressing a gap in operator learning methods often focused on continuous-time problems. The model demonstrates particular effectiveness in system identification tasks involving stable rational systems with poles close to the unit circle and long memory dynamics. AI

IMPACT Introduces a specialized neural operator for discrete-time system identification, potentially improving accuracy for specific dynamic systems.

RANK_REASON The cluster contains an arXiv preprint detailing a new model architecture for discrete-time dynamic systems.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

ZNO introduces stable rational neural operators for discrete-time dynamics

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The cluster contains an arXiv preprint detailing a new model architecture for discrete-time dynamic systems.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Xianli Zhu, Jia Yin ·

    ZNO: Stable Rational Neural Operators in the Z-Domain for Discrete-Time Dynamic

    arXiv:2605.02356v1 Announce Type: new Abstract: We introduce the Z-Domain Neural Operator (ZNO), a causal neural operator whose layers are stable low-rank multiple-input multiple-output (MIMO) rational filters parameterized directly in the $z$-plane. ZNO addresses a limitation of…

  2. arXiv cs.LG TIER_1 English(EN) · Jia Yin ·

    ZNO: Stable Rational Neural Operators in the Z-Domain for Discrete-Time Dynamic

    We introduce the Z-Domain Neural Operator (ZNO), a causal neural operator whose layers are stable low-rank multiple-input multiple-output (MIMO) rational filters parameterized directly in the $z$-plane. ZNO addresses a limitation of existing operator learning methods, many of whi…

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