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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…

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…

application deadline 28.11.2021

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.…

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…

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.…

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…

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…

More details here Github repository here

Tobias Bremicker presents his M.Sc. thesis on "Neural Stochastic Differential Equations"

Jannes Papenbrock presents his Semester Thesis on "Augmenting Physical Models with Machine Learning"

Luca Berardocco defends his Ph.D. thesis on " Hybridizable Discountinous Galerkin Methods for time-domain electromagnetic diffusion in underground strata formation"

A Ph.D. position in collaboration with BAM (host) at the interface of physical modeling and machine learning is available immediately

Marc Sebastián Padrós presents his M.Sc. thesis on "Parameter Identification for Thermal Reduced-Order Models in Electric Engines"

Jonas Eichelsdörfer presents his M.Sc. thesis on "Physics Informed Hamiltonian Neural Networks for System Identification"

Zhiyi He presents her M.Sc. Thesis on "Bayesian Generative Adversarial Networks for Medical Image Synthesis: A Comparative Study"

Simon Jarvers presents his B.Sc. thesis on "Machine Learning of ODE with Gaussian Processes"

Paper on "Physics-aware, probabilistic model order reduction with guaranteed stability" " accepted for ICLR 2021

New work on "A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables"