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New Weak-Pareto method robustly discovers fractional differential equations from noisy data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method called Weak-Pareto for discovering fractional differential equations from noisy data. This approach combines a weak formulation of fractional terms with Pareto-based subset selection to identify derivative orders and term types. Weak-Pareto demonstrates robustness against noise, outperforming traditional strong-form methods and a contemporary neural baseline in benchmark tests. AI

IMPACT Introduces a novel method for robustly identifying complex differential equations from noisy data, potentially improving scientific modeling and simulation.

RANK_REASON The item is a research paper detailing a new method for discovering fractional differential equations. [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 Weak-Pareto method robustly discovers fractional differential equations from noisy data

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The item is a research paper detailing a new method for discovering fractional differential equations. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Pongpisit Thanasutives, Yoshinobu Kawahara ·

    Robust data-driven discovery of fractional differential equations via weak formulations and Pareto-based subset selection

    arXiv:2608.12879v1 Announce Type: new Abstract: Fractional partial differential equations describe nonlocal dynamics, but discovering them from noisy data is difficult because fractional differentiation amplifies high-frequency measurement noise and the derivative orders are unkn…

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