遺伝的プログラミングによる支配方程式の推定

Translated title of the contribution: Rediscovery of governing equations from simulation data using Genetic Programming

小野 謙二, 古賀 壱成

    Research output: Contribution to journalArticlepeer-review

    Abstract

    <p>With advances in computers, observation, and simulation technology, it becomes an era when a large amount of data is generated, and it is becoming more important to find out the meaning and knowledge contained in the data. In this paper, we formulated the process of finding the governing equation describing the given data as a symbolic regression problem. In the proposed method, "partial differential function" is introduced into Genetic Programming to generate partial differential equations automatically, and the generated equations and data are compared and evaluated to automatically distill equations with less error. We conducted numerical experiments to estimate the governing equation from fluid simulation data and evaluated the validity of the proposed method. As a result, the original equation was obtained with high probability, and it was found that the proposed method becomes an effective tool to find useful modeling to represent the data.</p>
    Translated title of the contributionRediscovery of governing equations from simulation data using Genetic Programming
    Original languageJapanese
    Article number20201004
    Pages (from-to)1-10
    Number of pages10
    JournalTransactions of the Japan Society for Computational Engineering and Science
    Volume2020
    Issue number1
    DOIs
    Publication statusPublished - Jan 2020

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