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Technical University of Munich

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New paper in Acta Materialia on "PSP-GEN: Stochastic inversion of the Process–Structure–Property chain in materials design through deep, generative probabilistic modeling"

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…

New paper in CMAME on "Weak neural variational inference for solving Bayesian inverse problems without forward models: Applications in elastography"

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…

Radu Vasilache presents his B.Sc thesis on "Material Behavior Prediction using Physics-Informed Neural Networks and Fourier Neural Operators"

New work on "PSP-GEN: Stochastic inversion of the Process-Structure-Property chain in materials design through deep, generative probabilistic modeling"

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

New work on "Weak neural variational inference for solving Bayesian inverse problems without forward models: applications in elastography"

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

Sven Lüpke presents his Interdisciplinary Project on "Generative Modeling for Molecules"

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…

3 M.Sc thesis topics in collaboration with Carl Zeiss AG at the interface of Machine Learning and Physical Modeling

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.…

M.Sc. thesis on the topic of "Inverse Materials Design"

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

New paper on "A probabilistic, data-driven closure model for RANS simulations with aleatoric, model uncertainty" in the Journal of Computaional Physics

Highlights • We propose a probabilistic, data-driven closure model for Reynolds-Averaged Navier-Stokes simulations. • It is trained with high-fidelity data of mean velocity/pressure and quantifies aleatoric uncertainties in the model form. • It can automatically identify regions where closure is…

Maximilian Rixner defends his Ph.D. thesis on "Physics-Informed and Data-Driven Probabilistic Modeling of Materials Systems"

Abstract: Random media and the process-structure-property chain generally define complex, high-dimensional and stochastic materials systems, posing a challenging setting for any prediction or optimization task. In this thesis, we pursue a Bayesian approach for learning and predicting the behavior…

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