Hans Lieb

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Probabilistic Autoencoder Using Fisher Information

Neural networks play a growing role in many scientific disciplines, including physics. Variational autoencoders (VAEs) are neural networks that are able to represent the essential information of a high dimensional data set in a low dimensional latent space, which have a probabilistic interpretation. In particular, the so-called encoder network, the first part of the VAE, which maps its input onto a position in latent space, additionally provides uncertainty information in terms of variance around this position. In this work, an extension to the autoencoder architecture is introduced, the FisherNet. In this architecture, the latent space uncertainty is not generated using an additional information channel in the encoder but derived from the decoder by means of the Fisher information metric. This architecture has advantages from a theoretical point of view as it provides a direct uncertainty quantification derived from the model and also accounts for uncertainty cross-correlations. We can show experimentally that the FisherNet produces more accurate data reconstructions than a comparable VAE and its learning performance also apparently scales better with the number of latent space dimensions.     

First-principles lattice dynamics,thermodynamics, and elasticity of Cr2O3

We present the calculation of the lattice dynamics of chromia (Cr₂O₃), a typical Mott–Hubbard insulator, employing the first-principles density functional theory plus U approach. We first report the phonon dispersions at the theoretical equilibrium volume. Then the phonon density-of-states is calculated as a function of volume. Finally, the atomic volume, heat capacity, linear thermal expansion coefficient, bulk modulus, Grüneisen constant, and elastic constants are calculated as functions of temperature.     

Anton A. Benedetti-Pichler — 70 Jahre

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