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Siyuan Xing and Jian-Qiao Sun
The Gaussian-radial-basis function neural network (GRBFNN) has been a popular choice for interpolation and classification. However, it is computationally intensive when the dimension of the input vector is high. To address this issue, we propose a new fe...
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Lars Radtke, Georgios Bletsos, Niklas Kühl, Tim Suchan, Thomas Rung, Alexander Düster and Kathrin Welker
In the last decade, parameter-free approaches to shape optimization problems have matured to a state where they provide a versatile tool for complex engineering applications. However, sensitivity distributions obtained from shape derivatives in this cont...
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Peter Marvin Müller, Georgios Bletsos and Thomas Rung
The contribution is devoted to combined shape- and mesh-update strategies for parameter-free (CAD-free) shape optimization methods. Three different strategies to translate the shape sensitivities computed by adjoint shape optimization procedures into sim...
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George Tzougas and Konstantin Kutzkov
We developed a methodology for the neural network boosting of logistic regression aimed at learning an additional model structure from the data. In particular, we constructed two classes of neural network-based models: shallow?dense neural networks with ...
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Chao Wang, Jianhui Xu, Yuefeng Li, Tuanhui Wang and Qiwei Wang
Rockbursts are serious threats to the safe production of mining, resulting in great casualties and property losses. The accurate prediction of rockburst is an important premise that influences the safety and health of miners. As a classical machine learn...
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