TY  - JOUR
A1  - Rabczuk, Timon
A1  - Guo, Hongwei
A1  - Zhuang, Xiaoying
A1  - Chen, Pengwan
A1  - Alajlan, Naif
T1  - Stochastic deep collocation method based on neural architecture search and transfer learning for heterogeneous porous media
JF  - Engineering with Computers
N2  - We present a stochastic deep collocation method (DCM) based on neural architecture search (NAS) and transfer learning for heterogeneous porous media. We first carry out a sensitivity analysis to determine the key hyper-parameters of the network to reduce the search space and subsequently employ hyper-parameter optimization to finally obtain the parameter values. The presented NAS based DCM also saves the weights and biases of the most favorable architectures, which is then used in the fine-tuning process. We also employ transfer learning techniques to drastically reduce the computational cost. The presented DCM is then applied to the stochastic analysis of heterogeneous porous material. Therefore, a three dimensional stochastic flow model is built providing a benchmark to the simulation of groundwater flow in highly heterogeneous aquifers. The performance of the presented NAS based DCM is verified in different dimensions using the method of manufactured solutions. We show that it significantly outperforms finite difference methods in both accuracy and computational cost.
KW  - Maschinelles Lernen
KW  - Neuronales Lernen
KW  - Fehlerabschätzung
KW  - deep learning
KW  - neural architecture search
KW  - randomized spectral representation
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20220209-45835
UR  - https://link.springer.com/article/10.1007/s00366-021-01586-2
VL  - 2022
SP  - 1
EP  - 26
PB  - Springer
CY  - London
ER  - 
TY  - JOUR
A1  - Faizollahzadeh Ardabili, Sina
A1  - Najafi, Bahman
A1  - Alizamir, Meysam
A1  - Mosavi, Amir
A1  - Shamshirband, Shahaboddin
A1  - Rabczuk, Timon
T1  - Using SVM-RSM and ELM-RSM Approaches for Optimizing the Production Process of Methyl and Ethyl Esters
JF  - Energies
N2  - The production of a desired product needs an effective use of the experimental model. The present study proposes an extreme learning machine (ELM) and a support vector machine (SVM) integrated with the response surface methodology (RSM) to solve the complexity in optimization and prediction of the ethyl ester and methyl ester production process. The novel hybrid models of ELM-RSM and ELM-SVM are further used as a case study to estimate the yield of methyl and ethyl esters through a trans-esterification process from waste cooking oil (WCO) based on American Society for Testing and Materials (ASTM) standards. The results of the prediction phase were also compared with artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS), which were recently developed by the second author of this study. Based on the results, an ELM with a correlation coefficient of 0.9815 and 0.9863 for methyl and ethyl esters, respectively, had a high estimation capability compared with that for SVM, ANNs, and ANFIS. Accordingly, the maximum production yield was obtained in the case of using ELM-RSM of 96.86% for ethyl ester at a temperature of 68.48 °C, a catalyst value of 1.15 wt. %, mixing intensity of 650.07 rpm, and an alcohol to oil molar ratio (A/O) of 5.77; for methyl ester, the production yield was 98.46% at a temperature of 67.62 °C, a catalyst value of 1.1 wt. %, mixing intensity of 709.42 rpm, and an A/O of 6.09. Therefore, ELM-RSM increased the production yield by 3.6% for ethyl ester and 3.1% for methyl ester, compared with those for the experimental data.
KW  - Biodiesel
KW  - Optimierung
KW  - extreme learning machine
KW  - machine learning
KW  - response surface methodology
KW  - support vector machine
KW  - OA-Publikationsfonds2018
Y1  - 2018
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20181025-38170
UR  - https://www.mdpi.com/1996-1073/11/11/2889
IS  - 11, 2889
SP  - 1
EP  - 20
PB  - MDPI
CY  - Basel
ER  -