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Machine learning models accurately predict planetary fluid dynamics over long timescales

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel approach to simulating fluid dynamics in planetary interiors by employing online end-to-end machine learning. This method utilizes a differentiable solver that actively participates in the training process, enabling the learning of subgrid-scale models. The system was tested on two-dimensional quasi-geostrophic turbulence within a bounded annular domain, successfully reproducing complex phenomena like zonal jets and Rossby waves. A key finding is that a model trained on a single turnover time can accurately predict long-term dynamical behaviors occurring on timescales an order of magnitude longer than the training period. AI

IMPACT This research demonstrates a new method for simulating complex fluid dynamics, potentially accelerating geophysical research beyond the limits of direct numerical simulations.

RANK_REASON The cluster contains an academic paper detailing a new research methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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

Machine learning models accurately predict planetary fluid dynamics over long timescales

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The cluster contains an academic paper detailing a new research methodology. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Hugo Frezat, Thomas Gastine, Alexandre Fournier ·

    Online learning of subgrid-scale models for quasi-geostrophic turbulence in planetary interiors

    arXiv:2511.14581v3 Announce Type: replace-cross Abstract: Machine learning approaches to subgrid-scale (SGS) modeling are now well established in atmospheric and oceanic applications. Among these, online end-to-end learning, where the differentiable solver participates in the tra…

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