Abstract: In recent years, data-driven approaches have significantly reduced the computational effort required to capture the structure-property linkages in high-contrast microstruc- tures. Convolutional neural networks (CNNs), a special form of artificial neural networks (ANNs), have been shown to… [read more]

[read more]

Abstract: The thesis on hand deals with the implementation and investigation of the APHINITY frame- work proposed by Gallinari et al. [24]. The framework provides an unique decomposition of a complex dynamical system into a model based part and into an augmenting machine learn- ing part. While the… [read more]

Abstract: The simulation of electromagnetic phenomena in geophysical applications has benefited from the continued evolution of computers and numerical methods. However, despite the great success that high-order methods experienced in other engineering fields over the last decades, their… [read more]

application deadline 28.11.2021 [read more]

Abstract:  One part of the validation process of electric engines must check for thermal aging and damage of their components due to the high temperatures to which they are exposed. This way, the thermal requirements of the machine can be defined, and specific minimum service life can be guaranteed.… [read more]

Abstract: Neural networks excel at finding patterns in large amounts of data, yet they struggle to learn the basic laws of physics. Applying the methods of machine learning to build accurate models of the world thus requires a strong inductive bias, e.g. a notion of symmetry, invariances or… [read more]

Abstract: Machine Learning (ML) is widely utilized in various fields to solve problems and assist research. In the field of medical image synthesis, Deep Learning (DL) shows great potential. Recently, different kinds of frameworks of Generative Adversarial Network (GAN) have been developed.… [read more]

Abstract: Gaussian processes are a well-studied stochastic machine learning method that has proven to be useful in many areas of application. They are flexible due to their non- parametric approach and are able to quantify the uncertainty of their predictions. This work describes the basic… [read more]

Our paper on "Physics-aware, probabilistic model order reduction with guaranteed stability" has been accepted to #ICLR2021.  This year's acceptance rate was 29%. Congratulations to Sebastian Kaltenbach for putting in all the hard work! More details can be found… [read more]