Thèse de doctorat : Università della Svizzera italiana, 2021 ; 2021INFO014.
This thesis explores the field of graph neural networks, a class of deep learning models designed to learn representations of graphs. We organise the work into two parts. In the first part, we focus on the essential building blocks of graph neural networks. We present three novel operators for learning graph representations: one graph convolutional layer and two methods for pooling. We put...
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Thèse de doctorat : Università della Svizzera italiana, 2021 ; 2021INFO013.
Recent years have seen the rapid growth in interest towards fractional calculus. Fractional calculus plays an important role in modelling anomalous diffusion phenomena, however closed-form analytical solutions of such equations are rarely available, hence numerical estimates are needed. In this thesis we consider various fractional diffusion equations (FDEs), where different fractional ...
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In: IMA Journal of Numerical Analysis, 2018, vol. 38, no. 2, p. 921-954
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In: Algorithmica, 2015, vol. 71, no. 2, p. 354-376
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In: Israel Journal of Mathematics, 2015, vol. 210, no. 1, p. 245-295
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In: Journal für die reine und angewandte Mathematik (Crelles Journal), 2017, vol. 2017, no. 725, p. 217-234
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In: Nonlinear Dynamics, 2015, vol. 81, no. 1-2, p. 77-96
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In: Communications in Mathematical Physics, 2015, vol. 336, no. 1, p. 1-26
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In: Probability Theory and Related Fields, 2015, vol. 163, no. 1-2, p. 89-121
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In: Computational Mechanics, 2015, vol. 56, no. 5, p. 785-793
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