Feasibility of neural network metamodels for emulation and sensitivity analysis of radionuclide transport models

Abstract

In this paper we compare the outputs of neural network metamodels with numerical solutions of differential equation models in modeling cesium-137 transportation in sand. Convolutional neural networks (CNNs) were trained with differential equation simulation results. Training sets of various sizes (from 5120 to 163,840) were used. First order and total order Sobol methods were applied to both models in order to test the feasibility of neural network metamodels for sensitivity analysis of a radionuclide transport model. Convolutional neural networks were found to be capable of emulating the differential equation models with high accuracy when the training set size was 40,960 or higher. Neural network metamodels also gave similar results compared with the numerical solutions of the partial differential equation model in sensitivity analysis.

Crosstalk tolerance analysis of coupled-line structures using least square-support vector machine technique

“…Moreover, these phenomena initiate significant variations in the required response and in turn, it is needed to accurately predict the sensitivity analysis of the structure along the design procedure. For electromagnetic applications, sensitivity analysis, design exploration, and optimization are essential during the design process 2 , 3 .…”

Long-term, sustainable solutions to radioactive waste management

“…To overcome this issue surrogate model can be used. Turunen and Lipping 10 have built surrogate models using convolutional neural networks and used the meta model for a sensitivity analysis of the radionuclide transport models. In a separate work, Butorin and the team 11 investigated the effect of carbon content on the electronic structure of uranium carbides through a combination of XAS and density functional theory that takes 5f–5f Coulomb interaction U and spin–orbit coupling (DFT + U + SOC) into consideration.…”