Abstract: Primary production of metallic iron accounts for a significant share of the industrial green- house gas emissions, and an expected growth in the demand for steel motivates the search for alternative sustainable ways of iron ore reduction. Hydrogen-based direct reduction is a very… [weiterlesen]

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… [weiterlesen]

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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… [weiterlesen]

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… [weiterlesen]

application deadline 28.11.2021 [weiterlesen]

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.… [weiterlesen]

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… [weiterlesen]

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.… [weiterlesen]

Abstract The availability of high-performance computational resources have increased steadily, but we are still far form the capacity to perform high-fidelity simulations for turbulent flows in real-words applications. Thus, we still rely on computationally cheaper surrogates like Reynolds-Averaged… [weiterlesen]