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A DGTD method for the numerical modeling of the interaction of light with nanometer scale metallic structures taking into account non-local dispersion effects.

, , , , and . J. Comput. Phys., (2016)

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Using Deep Learning for Mobile Marketing User Conversion Prediction., , , and . IJCNN, page 1-8. IEEE, (2019)Open source evolutionary structured optimization., , , , , and . GECCO Companion, page 1599-1607. ACM, (2020)Optimal estimation for Large-Eddy Simulation of turbulence and application to the analysis of subgrid models, , and . CoRR, (2006)Black-Box Optimization Revisited: Improving Algorithm Selection Wizards Through Massive Benchmarking., , , , , , , and . IEEE Trans. Evol. Comput., 26 (3): 490-500 (2022)A Comparison of Data-Driven Approaches for Mobile Marketing User Conversion Prediction., , , and . IEEE Conf. on Intelligent Systems, page 140-146. IEEE, (2018)A DGTD method for the numerical modeling of the interaction of light with nanometer scale metallic structures taking into account non-local dispersion effects., , , , and . J. Comput. Phys., (2016)Versatile black-box optimization., , , , , , and . GECCO, page 620-628. ACM, (2020)Black-Box Optimization Revisited: Improving Algorithm Selection Wizards through Massive Benchmarking., , , , , , , and . CoRR, (2020)A Deep Learning-Based Decision Support System for Mobile Performance Marketing., , , and . Int. J. Inf. Technol. Decis. Mak., 22 (2): 679-703 (March 2023)Using Deep Learning for Ordinal Classification of Mobile Marketing User Conversion., , , and . IDEAL (1), volume 11871 of Lecture Notes in Computer Science, page 60-67. Springer, (2019)