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Abstract:Heat conduction topology optimization plays an important role in the design of efficient thermal management structures in engineering fields such as electronic devices, automotive systems, and aerospace applications. Traditional model-driven topology optimization methods typically combine… [read more]

Abstract:Neural operators have emerged as a powerful framework for learning solution operators of parametric partial differential equations. Among these, MultiONet extends the DeepONet architecture by aggregating information across multiple network depths, improving predictive accuracy without… [read more]

Abstract:When it comes to developing a product, evaluating how long it can withstand a certain type of load is an essential aspect of the production process. The product must be very robust in order to ensure a reliable and safe experience for the end user. A common approach to analyzing the… [read more]

This Ph.D. project focuses on the data-driven modeling and optimization of structural foams usedin high-voltage battery systems for electric vehicles. As part of BMW’s passive safety and elec-trification strategy, the research aims to develop a multidisciplinary framework that combines… [read more]

Abstract:Heat transfer simulations are an essential tool in engineering. Traditionally, temperature distributions in a body are computed by solving the heat equation numerically with a fi-nite element method (FEM). However, when many simulations for different problem variants (e.g. for testing… [read more]

Abstract: Inverse microstructure design plays a central role in materials discovery, yet remains challenging due to the complexity of structure-property linkages and the scarcity of labeled training data. We propose Design-GenNO, a physics-informed generative neural operator framework that unifies… [read more]

A Ph.D. position is available in our group to contribute to the development of efficient, certifiable, and practical solutions for inverse problems in diagnostic biomechanics, with a particular focus on elastography. The project builds on our recently developed Weak Neural Variational… [read more]

Abstract:High-dimensional Bayesian inverse analysis (dim >> 100) is mostly unfeasible for computationally demanding, nonlinear physics-based high-fidelity (HF) models. Usually, the use of more efficient gradient-based inference schemes is impeded if the multi-physics models are provided by complex… [read more]