DU-NO: A Parameter-Efficient Double U-Shaped Neural Operator for Phase-Resolving Wave Modeling

📰 ArXiv cs.AI

Learn how DU-NO, a double U-shaped neural operator, achieves parameter-efficient phase-resolving wave modeling, enabling accurate and fast simulations for operational forecasting.

advanced Published 14 Sept 2026

Action Steps

Implement DU-NO using PyTorch or TensorFlow to leverage its parameter-efficient architecture
Apply DU-NO to wave modeling tasks, such as simulating wave shoaling, refraction, and breaking
Compare the performance of DU-NO with existing phase-resolving wave models, like FUNWAVE-TVD
Use DU-NO to generate ensemble forecasts and quantify uncertainty in wave modeling
Integrate DU-NO with real-time data assimilation systems for improved operational forecasting

Who Needs to Know This

Researchers and engineers working on wave modeling and simulation can benefit from this article, as it presents a novel approach to improving the efficiency and accuracy of phase-resolving wave models.

Key Insight

💡 DU-NO achieves solver-level accuracy at a fraction of the cost of traditional phase-resolving wave models, making it suitable for operational forecasting and ensemble simulations.

Full Article

Title: DU-NO: A Parameter-Efficient Double U-Shaped Neural Operator for Phase-Resolving Wave Modeling

Abstract:
arXiv:2609.12115v1 Announce Type: new Abstract: Phase-resolving wave models such as FUNWAVE-TVD are the accuracy standard for nearshore dynamics, resolving the shoaling, refraction, and breaking of individual waves, but their cost rules them out for the ensembles, uncertainty quantification, and real-time warning that operational forecasting demands. Neural operators promise solver-level accuracy at a fraction of that cost, yet on wave-dominated fields the accurate ones are large: hybrid spectra

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DU-NO: A Parameter-Efficient Double U-Shaped Neural Operator for Phase-Resolving Wave Modeling

Full Article

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