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New method models turbulent dynamics with symbolic regression and generative AI

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel two-stage framework to model turbulent dynamical systems by combining symbolic regression with generative models. The first stage, the Finite Expression Method (FEX), identifies the governing deterministic dynamics and external forcing terms without requiring predefined libraries. The second stage employs generative models to capture the residual stochastic components, thereby accurately predicting higher-order statistical moments. This integrated approach has been validated on stochastic triad models, successfully recovering interaction terms and forcing expressions while accurately predicting moments up to the fifth order. AI

IMPACT Introduces a novel hybrid AI approach for enhanced modeling of complex turbulent systems, potentially improving accuracy in predicting higher-order statistical moments.

RANK_REASON The cluster contains an academic paper detailing a new method for modeling turbulent dynamics. [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 →

New method models turbulent dynamics with symbolic regression and generative AI

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The cluster contains an academic paper detailing a new method for modeling turbulent dynamics. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Chunmei Wang ·

    The finite expression method for turbulent dynamics with high-order moment recovery

    Turbulent dynamical systems are characterized by nonlinear interactions and stochastic effects that generate coupled statistical quantities, such as non-zero higher-order moments, which are difficult to capture from data with accuracy. We propose a two-stage data-driven modeling …

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