Highlights • PSP-GEN integrates the entire PSP chain into one deep model, addressing complexities. • It employs a two-part latent space: one for microstructures and one for processing links. • Discrete-valued design is reformulated as continuous for gradient-based optimization. • PSP-GEN… [weiterlesen]

Highlights • Novel Bayesian method using ML and physics to solve model-based, inverse problems. • Uses weighted residuals as virtual observables, avoiding a classical forward model. • Solving inverse problems for ill-posed (insufficient bound. cond.) forward problems. • Demonstrations in… [weiterlesen]

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Highlights: • We propose a novel probabilistic framework for training physics-informed surrogates for high dimensional, parametric PDEs as those encountered in (random) heterogeneous materials. • It is trained exclusively by sub-sampling weighted residuals, • In the core of its architecture lies… [weiterlesen]

We solve optimization problems involving a real-world physics-based, stochastic, bck-box, multi-modal, and high dimensional simulators. More details here: https://hal.science/hal-04659802 [weiterlesen]

A framework for the stochastic inversion of Process-Structure-Property chain in the design of heterogeneous materials. More details: https://arxiv.org/abs/2408.01114 [weiterlesen]

We solve model-based Bayesian inverse problems without a forward model.We explore applications in elastography with the ultimate goal of enabling model-based diagnosis on hand-held devices by non-experts. More details here: https://arxiv.org/abs/2407.20697 [weiterlesen]

Abstract: The goal of inverse molecular design is the discovery of novel molecular structures that fulfill a set of target properties. In this project we explore the application of generative models and molecular graph representations for the development of solutions to the inverse molecular design… [weiterlesen]

We offer multiple opportunities for a M.Sc. thesis on several topics at the interface of Machine Learning and Physical Modeling. The projects would be contacted in close collaboration with Carl Zeiss AG. More details can be found here. If you are interested please send an Email to Prof.… [weiterlesen]

We are currently looking for M.Sc. students to work on the topic of "Inverse Materials Design".  This is an interdisciplinary topic that combines computational modeling of physical systems with adavnaced machine learning techniques. More details can be found here [weiterlesen]