지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2020.1
- 수록면
- 287 - 295 (9page)
이용수
초록· 키워드
오류제보하기
Group method of data handling (GMDH) is considered one of the earliest deep learning methods. Deep learning gained additional interest in today's applications due to its capability to handle complex and high dimensional problems. In this study, multi-layer GMDH networks are used to perform uncertainty quantification (UQ) and sensitivity analysis (SA) of nuclear reactor simulations. GMDH is utilized as a surrogate/metamodel to replace high fidelity computer models with cheap-to-evaluate surrogate models, which facilitate UQ and SA tasks (e.g. variance decomposition, uncertainty propagation, etc.). GMDH performance is validated through two UQ applications in reactor simulations: (1) low dimensional input space (two-phase flow in a reactor channel), and (2) high dimensional space (8-group homogenized cross-sections). In both applications, GMDH networks show very good performance with small mean absolute and squared errors as well as high accuracy in capturing the target variance. GMDH is utilized afterward to perform UQ tasks such as variance decomposition through Sobol indices, and GMDH-based uncertainty propagation with large number of samples. GMDH performance is also compared to other surrogates including Gaussian processes and polynomial chaos expansions. The comparison shows that GMDH has competitive performance with the other methods for the low dimensional problem, and reliable performance for the high dimensional problem
목차
등록된 정보가 없습니다.
참고문헌 (51)
참고문헌 신청
Y. LeCun / 2015 / Deep learning / Nature 521(7553):436
L. Deng / 2014 / Deep learning: methods and applications / Foundations and Trends® in Signal Processing 7(3-4):197~387
T. Poggio / 2017 / Why and when can deep-but not shallow-networks avoid the curse of dimensionality : a review / Int. J. Autom. Comput 14(5):503~519
S. M. Erfani / 2016 / High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning / Pattern Recognit 58:121~134
E. Keogh / 2011 / Encyclopedia of Machine Learning / Springer :257~258
L. Deng / 2014 / Deep learning: methods and applications / Foundations and Trends® in Signal Processing 7(3-4):197~387
T. Poggio / 2017 / Why and when can deep-but not shallow-networks avoid the curse of dimensionality : a review / Int. J. Autom. Comput 14(5):503~519
S. M. Erfani / 2016 / High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning / Pattern Recognit 58:121~134
E. Keogh / 2011 / Encyclopedia of Machine Learning / Springer :257~258
함께 읽어보면 좋을 논문
논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!
-
Modified GMDH networks for oilfield production prediction
Geosystem Engineering
2018 .01
-
GMDH를 이용한 운전자의 충돌 회피 행동 평가에 관한 연구
전기전자학회논문지
2018 .09
-
Nonlinear Identification of Electronic Brake Pedal Behavior Using Hybrid GMDH and Genetic Algorithm in Brake-By-Wire System
Journal of Electrical Engineering & Technology
2017 .05
-
Short-term Electric Load Forecasting Based on Wavelet Transform and GMDH
Journal of Electrical Engineering & Technology
2015 .05
-
Application of GMDH model for predicting the fundamental period of regular RC infilled frames
Steel and Composite Structures, An International Journal
2022 .01
-
Soft computing-based slope stability assessment: A comparative study
Geomechanics & engineering
2018 .01
-
A methodology for uncertainty quantifi cation and sensitivity analysis for responses subject to Monte Carlo uncertainty with application to fuel plate characteristics in the ATRC
Nuclear Engineering and Technology
2022 .03
-
Development of uncertainty quantification module for VVER analysis in STREAM/RAST-V two-step method
Nuclear Engineering and Technology
2024 .08
-
Uncertainty Quantification of a Rotating Beam considering Parameter Random Fields
대한기계학회 춘추학술대회
2015 .05
-
RANS uncertainty quantification study On turbulent convection problems using artificial neural networks
한국전산유체공학회 학술대회논문집
2022 .10
-
적합 직교 분해 및 다항식 혼돈 확장을 활용한 비침습 확률적 모델 축소 기법
대한기계학회 춘추학술대회
2021 .08
-
Surrogate based model calibration for pressurized water reactor physics calculations
Nuclear Engineering and Technology
2017 .01
-
Uncertainty quantification of PWR spent fuel due to nuclear data and modeling parameters
Nuclear Engineering and Technology
2021 .03
-
Simulation-based Anomaly Detection in Nuclear Reactors
대한산업공학회 추계학술대회 논문집
2020 .11
-
Two-dimensional C3N structures
한국고분자학회 학술대회 연구논문 초록집
2016 .10
-
Optimal design of the floor panel for an automotive platform under uncertainty of the vehicle length
Geomechanics & engineering
2018 .01
-
ACE surrogate Model–Based uncertainty and sensitivity analysis methods for severe accident codes
Nuclear Engineering and Technology
2024 .09
-
시뮬레이션 데이터 생성을 통한 기계학습 기반 원자로 노심 이상 탐지 방법론
대한산업공학회지
2021 .04
-
Application case for phase III of UAM-LWR benchmark: Uncertainty propagation of thermal-hydraulic macroscopic parameters
Nuclear Engineering and Technology
2020 .08
-
Structural reliability analysis using temporal deep learning-based model and importance sampling
Structural Engineering and Mechanics, An Int＇l Journal
2022 .11
이 논문의 저자 정보
최근 본 자료
댓글(0)
0