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Discontinuous Galerkin Model Order Reduction of Geometrically Parametrized Stokes Equation

The present work focuses on the geometric parametrization and the reduced order modeling of the Stokes equation. We discuss the concept of a parametrized geometry and its application within a reduced order modeling technique. The full order model is based on the discontinuous Galerkin method with an interior penalty formulation. We introduce the broken Sobolev spaces as well as the weak formulation required for an affine parameter dependency. The operators are transformed from a fixed domain ...

Clusters of Defects as a Possible Origin of Random Telegraph Signal in Imager Devices: a DFT based Study

The origin of the random telegraph signal (RTS) observed in semiconductors-based electronic devices is still subject to debates. In this work, by means of atomistic simulations, typical clusters of defects as could be obtained after irradiation or implantation are studied as a possible cause for RTS. It is shown that:(i) a cluster of defects is highly metastable,(ii) it introduces several electronic states in the band gap,(iii) it has an electronic cross section much higher than the one of po...

Developing a Neural Network potential to investigate interface phenomena in solid-phase epitaxy

In this work, we develop a new neural network potential for silicon and perform accurate molecular dynamics simulations of the liquid, amorphous and diamond phases. The potential is tested against several physical properties and the solid phase epitaxy process is simulated.

Synthesizing realistic neural population activity patterns using generative adversarial networks

The ability to synthesize realistic patterns of neural activity is crucial for studying neural information processing. Here we used the Generative Adversarial Networks (GANs) framework to simulate the concerted activity of a population of neurons. We adapted the Wasserstein-GAN variant to facilitate the generation of unconstrained neural population activity patterns while still benefiting from parameter sharing in the temporal domain. We demonstrate that our proposed GAN, which we termed Spik...

Effect of Geometric Complexity on Intuitive Model Selection

Occam’s razor is the principle stating that, all else being equal, simpler explanations for a set of observations are to be preferred to more complex ones. This idea can be made precise in the context of statistical inference, where the same quantitative notion of complexity of a statistical model emerges naturally from different approaches based on Bayesian model selection and information theory. The broad applicability of this mathematical formulation suggests a normative model of decision-...

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