The research group Applied Mathematics and Machine Learning at the IDea_Lab of the University of Graz works at the interface of data science, machine learning, inverse problems and image processing. Within these fields, our research is characterized by a close connection of the development and analysis of mathematical models in function space with concrete, interdisciplinary applications.
IDea_Lab - The Interdisciplinary Digital Lab at the University of Graz
Leechgasse 34
A-8010 Graz
Austria
| phone (Secretary): | +43 316 380 - 1177 |
|---|---|
| phone (Head): | +43 316 380 - 1645 |
| mail (Secretary): | tanja.weiss(at)uni-graz.at |
| mail (Head): | martin.holler(at)uni-graz.at |
| 2026 | 07. September | 14:30 | SR 127.11, IDea_Lab | Luca Calatroni (University of Genoa): Learning spatially adaptive regularisation for computational imaging problemsAbstract: Modern computational imaging increasingly calls for methods that combine physical models of image formation with the flexibility of data-driven learning. In this talk, I will present an overview of my research at this interface, focusing on hybrid reconstruction methods that retain the mathematical structure and interpretability of variational models while learning problem-dependent components.I will take as an example the spatially adaptive Total Variation (TV) and Total Generalised Variation (TGV) regularisation models, in which regularisation strength varies across the image to better preserve edges, smooth regions, and fine details. I will discuss different strategies for estimating these parameter maps, ranging from statistical principles to bilevel optimisation and neural networks embedded within unrolled reconstruction algorithms. Applications to image denoising and accelerated MRI will illustrate how physics-aware learning can produce flexible and interpretable reconstruction methods, even when reference images or large training datasets are unavailable. |
| 2026 | 24. August | 14:30 | SR 127.11, IDea_Lab | Philipp Guth & Jesper Schröder (RICAM Linz): Dynamic output-feedback stabilization of uncertain linear dynamics via digital twinsAbstract: Differential equations with uncertain parameters provide a natural framework for modeling incomplete knowledge, measurement errors, and external disturbances. In control applications, such uncertainties can significantly affect system dynamics and pose challenges for the design of reliable feedback laws. This talk presents a computational framework for stabilization and control of uncertain linear dynamical systems, with a particular focus on the transition from offline uncertainty-aware controller design to online, data-driven system reconstruction.In the first part, we consider ensemble stabilization of linear systems with parametric uncertainty. Feedback laws are constructed using sampling-based approaches and generalized polynomial chaos expansions, yielding controllers that account for variability across the underlying parameter ensemble and provide robust closed-loop performance. The second part extends this framework toward digital twins by coupling feedback control with sequential Bayesian parameter estimation and observer design. A virtual model evolves alongside the physical system and simultaneously serves as an observer, parameter estimator, and feedback controller. Measurements from the controlled system are used to reconstruct its state and continuously update uncertain model parameters, while the virtual model generates stabilizing feedback. Numerical experiments illustrate the effectiveness of both approaches and demonstrate their potential for reliable closed-loop control in the presence of model uncertainty. |
| 2026 | 19. May | 14:30 | SR 127.11, IDea_Lab | Dr. Kostas Papafitsoros (Queen Mary University of London): Combining model-based regularisation with neural network-inferred spatiotemporally varying regularisation parameter maps for inverse imaging problemsAbstract: Combining model-based methods with data-driven approaches, typically based on deep learning, has become increasingly popular for solving inverse imaging problems, ranging from classical tasks such as image denoising to more advanced applications like dynamic Magnetic Resonance Image (MRI) reconstruction. On one hand, model-based methods offer interpretability and reconstruction guarantees. On the other hand, approaches relying on deep learning leverage large datasets as well as the versatility of neural networks to achieve state-of-the-art performance. Their combination seeks to bring together the strengths of both worlds. In this talk, we will present an overview of recent approaches that perform this combination by employing deep neural networks to infer spatially and also temporally adaptive regularisation maps. We will start with a general overview of theoretical properties of weighted versions of classical regularisers like Total Variation (TV) and Total Generalised Variation (TGV) focusing on the regularity of the regularisation parameter maps. We will then describe our main approach which employs a convolutional neural network to estimate these maps, combined with an unrolled algorithmic scheme to solve the image reconstruction problem. For the latter, we consider several model-based approaches including TV, TGV and convolutional synthesis regularisation. We discuss both supervised and self-supervised strategies for training the overall network and we provide numerical results for image denoising and (dynamic) MRI. |
| 2026 | 24. August |
Bruno Viti and Hendrik Kleikamp contributed to a new preprint, written together with Dilara Kılınç from Budapest University of Technology and Economics, that is now available on arXiv under the title Robust lane-change intention anticipation under uncertainty based on a recursive Bayesian filtering approach. The work mainly emerged during a Short Term Scientific Mission of Dilara Kılınç at IDea_Lab from June 29 to July 10, which was supported by the InterCoML COST Action. |
| 2026 | 17. August | Erion Morina is visiting the Cambridge Image Analysis Group of Prof. Carola-Bibiane Schönlieb at the Department of Applied Mathematics and Theoretical Physics, University of Cambridge, from August 17 to October 10, 2026. The research stay is funded by the "Visiting Award for High Potentials 2026", which he received from the University of Graz. |
| 2026 | 12. August | |
| 2026 | 29. July | A new preprint on Data-Driven Model Order Reduction with pyMOR by Petar Mlinarić, Stephan Rave, Felix Schindler and Hendrik Kleikamp, is available on arXiv. |
| 2026 | 22. July |
Matthias Höfler, Erion Morina and Hendrik Kleikamp presented their work at the World Congress on Computational Mechanics and the European Congress on Computational Methods in Applied Sciences and Engineering (WCCM-ECCOMAS) in Munich. |
