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2023 Vol.28, Issue 4 Preview Page

Article

Comparison of the Characteristics between the Dynamical Model and the Artificial Intelligence Model of the Lorenz System

Lorenz 시스템의 역학 모델과 자료기반 인공지능 모델의 특성 비교

YOUNG HO KIM1*
,
NAKYOUNG IM2
,
MIN WOO KIM2
,
JAE HEE JEONG2
,
EUN SEO JEONG2
김 영호1*
,
임 나경2
,
김 민우2
,
정 재희2
,
정 은서2
1Assistant Professor, Division of Earth Environmental System Science (Major of Oceanography), Pukyong National University, Busan 48513, Korea
2Undergraduate Student, Division of Earth Environmental System Science (Major of Oceanography), Pukyong National University, Busan 48513, Korea
1부경대학교 지구환경시스템과학부(해양학전공) 조교수
2부경대학교 지구환경시스템과학부(해양학전공) 학부생
*Corresponding Author
30 November 2023. pp. 133-142
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Abstract

In this paper, we built a data-driven artificial intelligence model using RNN-LSTM (Recurrent Neural Networks-Long Short-Term Memory) to predict the Lorenz system, and examined the possibility of whether this model can replace chaotic dynamic models. We confirmed that the data-driven model reflects the chaotic nature of the Lorenz system, where a small error in the initial conditions produces fundamentally different results, and the system moves around two stable poles, repeating the transition process, the characteristic of “deterministic non-periodic flow”, and simulates the bifurcation phenomenon. We also demonstrated the advantage of adjusting integration time intervals to reduce computational resources in data-driven models. Thus, we anticipate expanding the applicability of data-driven artificial intelligence models through future research on refining data-driven models and data assimilation techniques for data-driven models.

Keywords
Machine learning
Artificial intelligence
RNN-LSTM
Lorenz system
Chaotic

이 논문에서는 RNN (Recurrent Neural Networks)-LSTM (Long Short-Term Memory) 을 적용하여 Lorenz 시스템을 예측하는 자료기반 인공지능 모델을 구축하고, 이 모델이 미분방정식을 차분화하여 해를 구하는 역학 모델을 대체할 수 있는지 가능성을 진단하였다. 구축된 자료기반 모델이 초기 조건의 작은 교란이 근본적으로 다른 결과를 만들어내는 Lorenz 시스템의 카오스적인 특성을 반영한다는 것과, 시스템의 안정적인 두 개의 닻을 중심으로 운동하면서 전이 과정을 반복하는 특성, “결정론적 불규칙 흐름”의 특성, 분기 현상을 모사한다는 것을 확인하였다. 또한, 적분 시간 간격을 조절함으로써 전산자원을 절감할 수 있는 자료기반 모델의 장점을 보였다. 향후 자료기반 모델의 정교화와 자료기반 모델을 위한 자료동화 기법의 연구를 통해 자료기반 인공지능 모델의 활용성을 확대할 수 있을 것으로 기대한다.

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Information
  • Publisher :The Korean Society of Oceanography
  • Publisher(Ko) :한국해양학회
  • Journal Title :The Sea Journal of the Korean Society of Oceanography
  • Journal Title(Ko) :한국해양학회지 바다
  • Volume : 28
  • No :4
  • Pages :133-142
  • Received Date : 2023-08-29
  • Revised Date : 2023-11-13
  • Accepted Date : 2023-11-16
  • DOI :https://doi.org/10.7850/jkso.2023.28.4.133
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