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ScaleSplit-NO neural operator boosts 3D turbulence prediction efficiency

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel neural operator called ScaleSplit-NO, designed to improve the efficiency of predicting 3D turbulence. This method uses two operators: a Parent model for coarse-grained predictions and a Child model for high-resolution local details, conditioned on the Parent's output. This approach avoids operating on full-resolution fields, significantly reducing memory and data requirements. ScaleSplit-NO has demonstrated superior accuracy and data efficiency on turbulence benchmarks and has been applied to urban wind prediction in Montreal. AI

IMPACT Introduces a more memory and data-efficient approach for complex simulations, potentially accelerating scientific discovery and real-world applications like urban planning.

RANK_REASON Academic paper detailing a new method for turbulence prediction. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

ScaleSplit-NO neural operator boosts 3D turbulence prediction efficiency

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Academic paper detailing a new method for turbulence prediction. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Shaoxiang Qin, Yucheng Zhao, Zongyi Li, Liangzhu Leon Wang, Xiongye Xiao ·

    Scale-Split Neural Operator for Memory- and Data-Efficient 3D Turbulence Prediction

    arXiv:2609.38977v1 Announce Type: cross Abstract: Neural surrogates have emerged as fast alternatives to the numerical simulation of three-dimensional turbulence. However, training them at high resolution remains challenging, since the memory of full-field models grows with the r…

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