• Nuclear Engineering and Technology
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• v.52 no.2
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• pp.287-295
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• 2020

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.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.6
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• pp.711-717
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• 2014

Happiness has been studied in sociology and psychology as a matter of grave concern. In this paper the happiness model that a new second -order systems can be organized equivalently with a Spring-Damper-Mass are proposed. This model is organized a 2-dimensional model of identically type with Duffing equation. We added a nonlinear term to Duffing equation and also applied Gaussian white noise and period sine wave as external stimulus that is able to cause of happiness. Then we confirm that there are random motion, periodic motion and chaotic motion according to parameter variation in the new happiness model.