@inproceedings{62885, author = {{Osnabrügge, Malin and Tenberge, Claudia and Fechner, Sabine}}, keywords = {{Artificial intelligence, primary education, science and technology education}}, location = {{Norrköping, Sweden}}, title = {{{Artificial Intelligence in primary science and technology education with a focus on implementation of AI in a learning context – Results of a scoping review}}}, year = {{2026}}, } @book{67091, editor = {{Schulze, Max}}, isbn = {{978-3-95476-872-1}}, keywords = {{Malerei, Zeichnung}}, pages = {{308}}, publisher = {{Distanz}}, title = {{{ Ida Büngener 1963 – 2024 Selected Works }}}, year = {{2026}}, } @article{62948, author = {{Pollmeier, Pascal and Ponath, Jonas and Bohrmann-Linde, Claudia and Rubner, Isabel and Sommer, Katrin and Fechner, Sabine}}, journal = {{CHEMKON}}, keywords = {{Digital, Digitalisierung, Künstliche Intelligenz, KI, Messsensoren, Fortbildung, Lehrkräfte, Chemie}}, number = {{5}}, pages = {{138--144}}, title = {{{Digital und praxisnah: Was Chemielehrkräfte sich von (digitalisierungsbezogenen) Fortbildungen wünschen}}}, doi = {{https://doi.org/10.1002/ckon.70019}}, volume = {{33}}, year = {{2026}}, } @article{67066, author = {{Kullmer, Gunter and Krome, Sven and Ostwald, Richard}}, issn = {{0013-7944}}, journal = {{Engineering Fracture Mechanics}}, publisher = {{Elsevier BV}}, title = {{{A new approach for the formulaic description of the crack growth rate curve for long cracks in aluminum alloys}}}, doi = {{10.1016/j.engfracmech.2026.112568}}, volume = {{345}}, year = {{2026}}, } @book{62821, editor = {{Vogelsang, Christoph and Grotegut, Lea and Bruns, Julia and Riese, Josef and Fechner, Sabine}}, publisher = {{Waxmann}}, title = {{{Handlungsorientierung in der Ausbildung von Lehrkräften und pädagogischen Fachkräften}}}, doi = {{https://doi.org/10.31244/9783818851057}}, volume = {{2}}, year = {{2026}}, } @article{62957, author = {{Elsner, Julia and Tenberge, Claudia and Fechner, Sabine}}, journal = {{Zeitschrift für Didaktik der Naturwissenschaften}}, number = {{1}}, pages = {{1--17}}, title = {{{Modellieren und Denken im Diskontinuum}}}, doi = {{10.1007/s40573-026-00194-1}}, volume = {{32}}, year = {{2026}}, } @article{62956, author = {{Pollmeier, Pascal and Schulte, Talea and Ponath, Jonas and Fechner, Sabine}}, journal = {{Naturwissenschaften im Unterricht - Chemie}}, keywords = {{Digital, Digitalisierung, Nachhaltigkeit, Bildung für nachhaltige Entwicklung, BNE, Lernumgebungen}}, title = {{{Lernprozesse im Chemieunterricht durch kontextorientierte digitale Lernumgebungen mit Messwerterfassung unterstützen}}}, year = {{2026}}, } @inproceedings{67295, abstract = {{In increasingly volatile and uncertain markets, corporate resilience has become a critical capability in strategic product planning. Companies face significant challenges in systematically monitoring and interpreting heterogeneous environmental data originating from diverse sources, formats, and temporal contexts. While predefined workflows and decision trees can support strategic analysis, they often lack the flexibility required to cope with dynamic market conditions and foresightrelated data from extreme dispersed and heterogeneous sources. This paper proposes a method to enhance corporate resilience through the application of generic, reusable AI-based workflows in strategic product planning. The approach integrates Data Science and Artificial Intelligence methods into modular, visually modelled workflows that enable hybrid human-AI decision-making. Based on a systematic literature review and an analysis of industrial challenges, key success factors and resilience criteria are identified. These insights are used to develop a method that supports internal and external analyses, scenario-based strategy development, and adaptive implementation monitoring within a generic workflow structure. The method leverages techniques such as machine learning and generative AI to process structured and unstructured data, identify patterns, and support real-time strategic assessments. Validation with decision-makers from medium-sized companies demonstrates improved transparency, repeatability, and cross-functional collaboration compared to predefined workflows.}}, author = {{Gräßler, Iris and Özcan, Deniz}}, booktitle = {{1st International Symposium: March 24 – 26, 2026, Heinz Nixdorf Institute, Paderborn University}}, editor = {{Gräßler, Iris}}, location = {{Paderborn}}, publisher = {{LibreCat University}}, title = {{{Corporate resilience through generic AI-based workflows in strategic product planning}}}, doi = {{10.17619/UNIPB/1-2636}}, volume = {{1}}, year = {{2026}}, } @inproceedings{67300, abstract = {{Deep neural networks (DNNs) are vulnerable to small adversarial perturbations, which are tiny changes to the input data that appear insignificant but cause the model to produce drastically different outputs. Many defense methods require modifying model architectures during evaluation or performing test-time data purification. This not only introduces additional complexity but is often architecture-dependent. We show, however, that robust feature learning during training can significantly enhance DNN robustness. We propose MOREL, a multi-objective approach that aligns natural and adversarial features using cosine similarity and multi-positive contrastive losses to encourage similar features for same-class inputs. Extensive experiments demonstrate that MOREL significantly improves robustness against both white-box and black-box attacks. Our code is available at https://github.com/salomonhotegni/MOREL.}}, author = {{Hotegni, Sedjro Salomon and Peitz, Sebastian}}, booktitle = {{Artificial Neural Networks and Machine Learning – ICANN 2025}}, editor = {{Senn, Walter and Sanguineti, Marcello and Saudargiene, Ausra and Tetko, Igor V. and Villa, Alessandro E. P. and Jirsa, Viktor and Bengio, Yoshua}}, isbn = {{978-3-032-04558-4}}, keywords = {{own, own-conference}}, pages = {{442–454}}, publisher = {{Springer Nature Switzerland}}, title = {{{Enhancing Adversarial Robustness Through Multi-objective Representation Learning}}}, doi = {{10.1007/978-3-032-04558-4_35}}, year = {{2026}}, } @article{67302, abstract = {{Data-driven surrogate models provide fast and fully differentiable approximations of complex dynamical systems. In this work, we develop such surrogates for the Rayleigh–Bénard convection (RBC), which governs thermally driven flows in natural and industrial environments. Specifically, the proposed models approximate the discrete-time flow map of the RBC system, advancing the full system state by a fixed time step. We train Fourier Neural Operator (FNO)–based models to learn the dynamics of RBC in two and three dimensions and compare them to a convolutional U-Net baseline and a Koopman-based Linear Recurrent Autoencoder Network (LRAN). The two-dimensional system serves as a baseline for the more challenging three-dimensional case, which exhibits increased spatial complexity and turbulent dynamics. Across all settings, FNO-based models consistently outperform the LRAN, while achieving performance comparable to the U-Net in several regimes. Incorporating spatio-temporal inputs via FNOs leads to improved long-term prediction accuracy, particularly for turbulent flows. The physical fidelity of the predictions is assessed using convective heat flux statistics, profiles, and fluctuations, showing that FNOs most closely reproduce the ground-truth flow statistics. In addition, we demonstrate that FNOs enable zero-shot super-resolution across unseen spatial discretizations, a capability not shared by the convolutional baselines. These results highlight the potential of neural operator–based models as accurate, physically consistent, and resolution-independent surrogates for downstream tasks such as flow control.}}, author = {{Markmann, Thorben and Straat, Michiel and Peitz, Sebastian and Hammer, Barbara}}, issn = {{0925-2312}}, journal = {{Neurocomputing}}, keywords = {{own, own-journal, erc}}, pages = {{133201}}, title = {{{Fourier neural operators as data-driven surrogates for two- and three-dimensional Rayleigh–Bénard convection}}}, doi = {{10.1016/j.neucom.2026.133201}}, volume = {{679}}, year = {{2026}}, } @inproceedings{67299, author = {{Harder, Hans and Vishwasrao, Abhijeet and Guastoni, Luca and Vinuesa, Ricardo and Peitz, Sebastian}}, booktitle = {{Proceedings of The 8th Annual Learning for Dynamics and Control Conference}}, editor = {{Sukhatme, Gaurav and Lindemann, Lars and Tu, Stephen and Wierman, Adam and Atanasov, Nikolay}}, keywords = {{own, own-conference, erc}}, pages = {{1601–1619}}, publisher = {{PMLR}}, title = {{{Efficient probabilistic surrogate modeling techniques for partially-observed large-scale dynamical systems}}}, doi = {{10.48550/arXiv.2511.04641}}, volume = {{331}}, year = {{2026}}, } @inproceedings{67303, author = {{Amakor, Augustina Chidinma and Sonntag, Konstantin and Peitz, Sebastian}}, booktitle = {{European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)}}, keywords = {{own, own-conference}}, pages = {{652--668}}, title = {{{Interactive Pareto navigation for deep multi-task learning}}}, doi = {{10.1007/978-3-032-37667-1_37}}, year = {{2026}}, } @inproceedings{67305, author = {{Becktepe, Jannis and Franz, Aleksandra and Thuerey, Nils and Peitz, Sebastian}}, booktitle = {{International Conference on Machine Learning (ICML)}}, keywords = {{own, own-conference, erc}}, title = {{{Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control}}}, doi = {{10.48550/arXiv.2601.15015}}, year = {{2026}}, } @article{67301, author = {{Wohlleben, Meike Claudia and Schütte, Jan and Berkemeier, Manuel and Sextro, Walter and Peitz, Sebastian}}, journal = {{Multibody System Dynamics}}, keywords = {{own, own-journal}}, title = {{{Evaluating Physics-Based, Hybrid, and Data-Driven Models for Rubber-Metal Bushings}}}, doi = {{10.1007/s11044-026-10146-9}}, year = {{2026}}, } @unpublished{67298, author = {{Mugisho Zagabe, Christian and Peitz, Sebastian}}, booktitle = {{arXiv:2603.26464}}, keywords = {{own, own-preprint, erc}}, title = {{{Automatic feature identification in least-squares policy iteration using the Koopman operator framework}}}, year = {{2026}}, } @unpublished{67304, author = {{Plotzki, Tim and Peitz, Sebastian}}, booktitle = {{arXiv:2603.28074}}, keywords = {{own, own-preprint, erc}}, title = {{{Koopman-based surrogate modeling for reinforcement-learning-control of Rayleigh-Benard convection}}}, year = {{2026}}, } @article{62816, abstract = {{The increasing demand for advanced sensing technologies drives the development of chemical sensors using innovative materials. In gas sensing, optical sensors are often used to detect gases such as CO, NOx, and O2. Oxygen sensors typically incorporate dyes into oxygen-permeable matrices like polymers, silica, or zeolites. Alternatively, semiconductor surface chemistry can enable O2 detection. However, these approaches are often limited by slow response and recovery times and low selectivity, restricting their practical applications. The metal-organic framework MOF-76(Eu) and its yttrium-modified variant, MOF-76(Eu/Y) are reported to exhibit highly reversible and fast optical responses to varying O2 concentrations. Time-resolved emission measurements are performed over short (seconds) and long (hours) timescales using N2 and synthetic air mixtures. Cross-sensitivity to humidity is analyzed. Multichannel scaling photon-counting experiments confirm quenching at the linker level, as the emission lifetime remains nearly constant. Yttrium significantly improves stability and performance at room temperature. Structural and optical changes induced by yttrium are investigated. Additionally, MIL-78(Eu), another Eu-BTC-based MOF with a different coordination environment, is synthesized. Unlike MOF-76(Eu), MIL-78(Eu) exhibits distinct optical properties but lacks a reversible response to O2. These results highlight the potential of MOF-76-based materials for high-performance O2 sensing.}}, author = {{Zhao, Zhenyu and Weinberger, Christian and Steube, Jakob and Bauer, Matthias and Brehm, Martin and Tiemann, Michael}}, issn = {{1616-301X}}, journal = {{Advanced Functional Materials}}, publisher = {{Wiley}}, title = {{{Fast‐Responding O2 Gas Sensor Based on Luminescent Europium Metal‐Organic Frameworks (MOF‐76)}}}, doi = {{10.1002/adfm.202511190}}, year = {{2026}}, } @inproceedings{67339, abstract = {{Explainable artificial intelligence (XAI) is essential for critical domains such as healthcare and autonomous systems to build trust and confidence in real-world deployment. In this context, description logic knowledge bases (KBs) provide structured and semantically rich representations that support reasoning and informed decision-making. A core task in applying KBs to XAI is class expression learning (CEL), which generates explainable logical descriptions for classifying instances within KBs. Unlike black-box models with opaque internal mechanisms, CEL provides global explainability and ease of integration with domain knowledge. However, current approaches to CEL face significant limitations such as poor scalability, failure to capture rare patterns, and limited exploration of the vast class expression search space. To overcome these limitations, we introduce LYRA, a novel multi-agent deep reinforcement learning framework that formulates CEL as a collaborative planning task under uncertainty. The integration of the Dempster–Shafer theory enables agents to effectively reason under ambiguity and manage conflicting or inconsistent information. Our experiments show that LYRA outperforms state-of-the-art methods on seven out of eight datasets, demonstrating robust and scalable CEL. Additionally, LYRA offers interpretable decisions and employs advanced search strategies, enabling the discovery of more precise and expressive class expressions than existing approaches.}}, author = {{Abdulmaqsod, Amgad and Mahmood, Yasir and Ngonga Ngomo, Axel-Cyrille and Sherif, Mohamed Ahmed}}, booktitle = {{The Semantic Web – ISWC 2026}}, keywords = {{amgad dice enexa fairomics mahmood ngonga sailproject sherif simba whale}}, title = {{{LYRA: Belief-Driven Scalable Class Expression Learning in Description Logics}}}, year = {{2026}}, } @inproceedings{67344, author = {{Becker, Alexander and Ngonga Ngomo, Axel-Cyrille and Sherif, Mohamed}}, booktitle = {{The Semantic Web – 23rd European Semantic Web Conference, ESWC 2026, Dubrovnik , Croatia, May 10-14, 2026, Proceedings}}, keywords = {{becker dice enexa kiowl ngonga sailproject sherif trr318_inf whale}}, publisher = {{Springer Nature Switzerland}}, title = {{{TIM: Tiered Iterative Knowledge Graph Matching}}}, year = {{2026}}, } @inproceedings{67341, author = {{Ekinci Birol, Duygu and KOUAGOU, N'Dah Jean and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{IEEE International Conference on Data Mining (ICDM) 2026}}, keywords = {{dice duygu fairomics kouagou ngonga sail sherif trr318 whale}}, title = {{{ATLAS: Adaptive Attribute-Aware Post-Hoc Alignment of Knowledge Graph Embeddings}}}, year = {{2026}}, }