[{"publication":"Naturwissenschaften im Unterricht - Chemie","citation":{"apa":"Pollmeier, P., Schulte, T., Ponath, J., &#38; Fechner, S. (2026). Lernprozesse im Chemieunterricht durch kontextorientierte digitale Lernumgebungen mit Messwerterfassung unterstützen. <i>Naturwissenschaften Im Unterricht - Chemie</i>.","ieee":"P. Pollmeier, T. Schulte, J. Ponath, and S. Fechner, “Lernprozesse im Chemieunterricht durch kontextorientierte digitale Lernumgebungen mit Messwerterfassung unterstützen,” <i>Naturwissenschaften im Unterricht - Chemie</i>, 2026.","chicago":"Pollmeier, Pascal, Talea Schulte, Jonas Ponath, and Sabine Fechner. “Lernprozesse Im Chemieunterricht Durch Kontextorientierte Digitale Lernumgebungen Mit Messwerterfassung Unterstützen.” <i>Naturwissenschaften Im Unterricht - Chemie</i>, 2026.","short":"P. Pollmeier, T. Schulte, J. Ponath, S. Fechner, Naturwissenschaften Im Unterricht - Chemie (2026).","mla":"Pollmeier, Pascal, et al. “Lernprozesse Im Chemieunterricht Durch Kontextorientierte Digitale Lernumgebungen Mit Messwerterfassung Unterstützen.” <i>Naturwissenschaften Im Unterricht - Chemie</i>, 2026.","ama":"Pollmeier P, Schulte T, Ponath J, Fechner S. Lernprozesse im Chemieunterricht durch kontextorientierte digitale Lernumgebungen mit Messwerterfassung unterstützen. <i>Naturwissenschaften im Unterricht - Chemie</i>. Published online 2026.","bibtex":"@article{Pollmeier_Schulte_Ponath_Fechner_2026, title={Lernprozesse im Chemieunterricht durch kontextorientierte digitale Lernumgebungen mit Messwerterfassung unterstützen}, journal={Naturwissenschaften im Unterricht - Chemie}, author={Pollmeier, Pascal and Schulte, Talea and Ponath, Jonas and Fechner, Sabine}, year={2026} }"},"quality_controlled":"1","project":[{"name":"ComeMINT-Netzwerk. fortbilden durch vernetzen – vernetzen durch fortbilden. Gelingensbedingungen adaptiver MINT-Fortbildungsmodule in Community Networks.","_id":"641"}],"date_created":"2025-12-08T09:30:27Z","keyword":["Digital","Digitalisierung","Nachhaltigkeit","Bildung für nachhaltige Entwicklung","BNE","Lernumgebungen"],"type":"journal_article","department":[{"_id":"386"}],"status":"public","year":"2026","title":"Lernprozesse im Chemieunterricht durch kontextorientierte digitale Lernumgebungen mit Messwerterfassung unterstützen","author":[{"first_name":"Pascal","last_name":"Pollmeier","full_name":"Pollmeier, Pascal","id":"44191"},{"first_name":"Talea","last_name":"Schulte","full_name":"Schulte, Talea"},{"full_name":"Ponath, Jonas","first_name":"Jonas","last_name":"Ponath","id":"100087"},{"id":"54823","orcid":"0000-0001-5645-5870","last_name":"Fechner","first_name":"Sabine","full_name":"Fechner, Sabine"}],"date_updated":"2026-09-30T17:15:46Z","publication_status":"published","article_type":"original","_id":"62956","language":[{"iso":"eng"}],"user_id":"54823"},{"department":[{"_id":"43"},{"_id":"9"},{"_id":"26"},{"_id":"152"}],"type":"conference","date_created":"2026-10-01T10:04:57Z","abstract":[{"lang":"eng","text":"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."}],"publication":"1st International Symposium: March 24 – 26, 2026, Heinz Nixdorf Institute, Paderborn University","doi":"10.17619/UNIPB/1-2636","language":[{"iso":"eng"}],"main_file_link":[{"open_access":"1","url":"https://digital.ub.uni-paderborn.de/hs/download/pdf/8359163"}],"intvolume":"         1","date_updated":"2026-10-01T10:14:12Z","author":[{"id":"47565","last_name":"Gräßler","orcid":"0000-0001-5765-971X","first_name":"Iris","full_name":"Gräßler, Iris"},{"id":"58595","last_name":"Özcan","first_name":"Deniz","full_name":"Özcan, Deniz"}],"title":"Corporate resilience through generic AI-based workflows in strategic product planning","year":"2026","oa":"1","quality_controlled":"1","citation":{"apa":"Gräßler, I., &#38; Özcan, D. (2026). Corporate resilience through generic AI-based workflows in strategic product planning. In I. Gräßler (Ed.), <i>1st International Symposium: March 24 – 26, 2026, Heinz Nixdorf Institute, Paderborn University</i> (Vol. 1). LibreCat University. <a href=\"https://doi.org/10.17619/UNIPB/1-2636\">https://doi.org/10.17619/UNIPB/1-2636</a>","ieee":"I. Gräßler and D. Özcan, “Corporate resilience through generic AI-based workflows in strategic product planning,” in <i>1st International Symposium: March 24 – 26, 2026, Heinz Nixdorf Institute, Paderborn University</i>, Paderborn, 2026, vol. 1, doi: <a href=\"https://doi.org/10.17619/UNIPB/1-2636\">10.17619/UNIPB/1-2636</a>.","chicago":"Gräßler, Iris, and Deniz Özcan. “Corporate Resilience through Generic AI-Based Workflows in Strategic Product Planning.” In <i>1st International Symposium: March 24 – 26, 2026, Heinz Nixdorf Institute, Paderborn University</i>, edited by Iris Gräßler, Vol. 1. LibreCat University, 2026. <a href=\"https://doi.org/10.17619/UNIPB/1-2636\">https://doi.org/10.17619/UNIPB/1-2636</a>.","short":"I. Gräßler, D. Özcan, in: I. Gräßler (Ed.), 1st International Symposium: March 24 – 26, 2026, Heinz Nixdorf Institute, Paderborn University, LibreCat University, 2026.","mla":"Gräßler, Iris, and Deniz Özcan. “Corporate Resilience through Generic AI-Based Workflows in Strategic Product Planning.” <i>1st International Symposium: March 24 – 26, 2026, Heinz Nixdorf Institute, Paderborn University</i>, edited by Iris Gräßler, vol. 1, LibreCat University, 2026, doi:<a href=\"https://doi.org/10.17619/UNIPB/1-2636\">10.17619/UNIPB/1-2636</a>.","ama":"Gräßler I, Özcan D. Corporate resilience through generic AI-based workflows in strategic product planning. In: Gräßler I, ed. <i>1st International Symposium: March 24 – 26, 2026, Heinz Nixdorf Institute, Paderborn University</i>. Vol 1. LibreCat University; 2026. doi:<a href=\"https://doi.org/10.17619/UNIPB/1-2636\">10.17619/UNIPB/1-2636</a>","bibtex":"@inproceedings{Gräßler_Özcan_2026, title={Corporate resilience through generic AI-based workflows in strategic product planning}, volume={1}, DOI={<a href=\"https://doi.org/10.17619/UNIPB/1-2636\">10.17619/UNIPB/1-2636</a>}, booktitle={1st International Symposium: March 24 – 26, 2026, Heinz Nixdorf Institute, Paderborn University}, publisher={LibreCat University}, author={Gräßler, Iris and Özcan, Deniz}, editor={Gräßler, Iris}, year={2026} }"},"editor":[{"last_name":"Gräßler","first_name":"Iris","full_name":"Gräßler, Iris"}],"volume":1,"user_id":"58595","_id":"67295","publisher":"LibreCat University","conference":{"location":"Paderborn","start_date":"2026-03-24","name":"International Symposium on Hybrid Intelligence in Product and Production Engineering 1. 2026 Paderborn","end_date":"2026-03-26"},"status":"public"},{"publication":"Artificial Neural Networks and Machine Learning – ICANN 2025","abstract":[{"lang":"eng","text":"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."}],"date_created":"2026-10-01T11:53:11Z","department":[{"_id":"655"}],"type":"conference","keyword":["own","own-conference"],"author":[{"full_name":"Hotegni, Sedjro Salomon","last_name":"Hotegni","first_name":"Sedjro Salomon","id":"97995"},{"orcid":"0000-0002-3389-793X","last_name":"Peitz","first_name":"Sebastian","full_name":"Peitz, Sebastian","id":"47427"}],"publication_identifier":{"isbn":["978-3-032-04558-4"]},"year":"2026","title":"Enhancing Adversarial Robustness Through Multi-objective Representation Learning","date_updated":"2026-10-01T11:53:46Z","language":[{"iso":"eng"}],"doi":"10.1007/978-3-032-04558-4_35","citation":{"bibtex":"@inproceedings{Hotegni_Peitz_2026, place={Cham}, title={Enhancing Adversarial Robustness Through Multi-objective Representation Learning}, DOI={<a href=\"https://doi.org/10.1007/978-3-032-04558-4_35\">10.1007/978-3-032-04558-4_35</a>}, booktitle={Artificial Neural Networks and Machine Learning – ICANN 2025}, publisher={Springer Nature Switzerland}, author={Hotegni, Sedjro Salomon and Peitz, Sebastian}, editor={Senn, Walter and Sanguineti, Marcello and Saudargiene, Ausra and Tetko, Igor V. and Villa, Alessandro E. P. and Jirsa, Viktor and Bengio, Yoshua}, year={2026}, pages={442–454} }","ama":"Hotegni SS, Peitz S. Enhancing Adversarial Robustness Through Multi-objective Representation Learning. In: Senn W, Sanguineti M, Saudargiene A, et al., eds. <i>Artificial Neural Networks and Machine Learning – ICANN 2025</i>. Springer Nature Switzerland; 2026:442–454. doi:<a href=\"https://doi.org/10.1007/978-3-032-04558-4_35\">10.1007/978-3-032-04558-4_35</a>","mla":"Hotegni, Sedjro Salomon, and Sebastian Peitz. “Enhancing Adversarial Robustness Through Multi-Objective Representation Learning.” <i>Artificial Neural Networks and Machine Learning – ICANN 2025</i>, edited by Walter Senn et al., Springer Nature Switzerland, 2026, pp. 442–454, doi:<a href=\"https://doi.org/10.1007/978-3-032-04558-4_35\">10.1007/978-3-032-04558-4_35</a>.","short":"S.S. Hotegni, S. Peitz, in: W. Senn, M. Sanguineti, A. Saudargiene, I.V. Tetko, A.E.P. Villa, V. Jirsa, Y. Bengio (Eds.), Artificial Neural Networks and Machine Learning – ICANN 2025, Springer Nature Switzerland, Cham, 2026, pp. 442–454.","chicago":"Hotegni, Sedjro Salomon, and Sebastian Peitz. “Enhancing Adversarial Robustness Through Multi-Objective Representation Learning.” In <i>Artificial Neural Networks and Machine Learning – ICANN 2025</i>, edited by Walter Senn, Marcello Sanguineti, Ausra Saudargiene, Igor V. Tetko, Alessandro E. P. Villa, Viktor Jirsa, and Yoshua Bengio, 442–454. Cham: Springer Nature Switzerland, 2026. <a href=\"https://doi.org/10.1007/978-3-032-04558-4_35\">https://doi.org/10.1007/978-3-032-04558-4_35</a>.","ieee":"S. S. Hotegni and S. Peitz, “Enhancing Adversarial Robustness Through Multi-objective Representation Learning,” in <i>Artificial Neural Networks and Machine Learning – ICANN 2025</i>, 2026, pp. 442–454, doi: <a href=\"https://doi.org/10.1007/978-3-032-04558-4_35\">10.1007/978-3-032-04558-4_35</a>.","apa":"Hotegni, S. S., &#38; Peitz, S. (2026). Enhancing Adversarial Robustness Through Multi-objective Representation Learning. In W. Senn, M. Sanguineti, A. Saudargiene, I. V. Tetko, A. E. P. Villa, V. Jirsa, &#38; Y. Bengio (Eds.), <i>Artificial Neural Networks and Machine Learning – ICANN 2025</i> (pp. 442–454). Springer Nature Switzerland. <a href=\"https://doi.org/10.1007/978-3-032-04558-4_35\">https://doi.org/10.1007/978-3-032-04558-4_35</a>"},"place":"Cham","status":"public","_id":"67300","publisher":"Springer Nature Switzerland","page":"442–454","editor":[{"full_name":"Senn, Walter","first_name":"Walter","last_name":"Senn"},{"full_name":"Sanguineti, Marcello","last_name":"Sanguineti","first_name":"Marcello"},{"last_name":"Saudargiene","first_name":"Ausra","full_name":"Saudargiene, Ausra"},{"last_name":"Tetko","first_name":"Igor V.","full_name":"Tetko, Igor V."},{"full_name":"Villa, Alessandro E. P.","first_name":"Alessandro E. P.","last_name":"Villa"},{"first_name":"Viktor","last_name":"Jirsa","full_name":"Jirsa, Viktor"},{"full_name":"Bengio, Yoshua","last_name":"Bengio","first_name":"Yoshua"}],"user_id":"47427"},{"doi":"10.1016/j.neucom.2026.133201","user_id":"47427","volume":679,"page":"133201","_id":"67302","language":[{"iso":"eng"}],"date_updated":"2026-10-01T11:56:04Z","intvolume":"       679","year":"2026","status":"public","title":"Fourier neural operators as data-driven surrogates for two- and three-dimensional Rayleigh–Bénard convection","publication_identifier":{"issn":["0925-2312"]},"author":[{"last_name":"Markmann","first_name":"Thorben","full_name":"Markmann, Thorben"},{"first_name":"Michiel","last_name":"Straat","full_name":"Straat, Michiel"},{"id":"47427","full_name":"Peitz, Sebastian","orcid":"0000-0002-3389-793X","last_name":"Peitz","first_name":"Sebastian"},{"full_name":"Hammer, Barbara","last_name":"Hammer","first_name":"Barbara"}],"type":"journal_article","keyword":["own","own-journal","erc"],"department":[{"_id":"655"}],"date_created":"2026-10-01T11:55:20Z","abstract":[{"text":"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.","lang":"eng"}],"publication":"Neurocomputing","citation":{"mla":"Markmann, Thorben, et al. “Fourier Neural Operators as Data-Driven Surrogates for Two- and Three-Dimensional Rayleigh–Bénard Convection.” <i>Neurocomputing</i>, vol. 679, 2026, p. 133201, doi:<a href=\"https://doi.org/10.1016/j.neucom.2026.133201\">10.1016/j.neucom.2026.133201</a>.","ama":"Markmann T, Straat M, Peitz S, Hammer B. Fourier neural operators as data-driven surrogates for two- and three-dimensional Rayleigh–Bénard convection. <i>Neurocomputing</i>. 2026;679:133201. doi:<a href=\"https://doi.org/10.1016/j.neucom.2026.133201\">10.1016/j.neucom.2026.133201</a>","bibtex":"@article{Markmann_Straat_Peitz_Hammer_2026, title={Fourier neural operators as data-driven surrogates for two- and three-dimensional Rayleigh–Bénard convection}, volume={679}, DOI={<a href=\"https://doi.org/10.1016/j.neucom.2026.133201\">10.1016/j.neucom.2026.133201</a>}, journal={Neurocomputing}, author={Markmann, Thorben and Straat, Michiel and Peitz, Sebastian and Hammer, Barbara}, year={2026}, pages={133201} }","apa":"Markmann, T., Straat, M., Peitz, S., &#38; Hammer, B. (2026). Fourier neural operators as data-driven surrogates for two- and three-dimensional Rayleigh–Bénard convection. <i>Neurocomputing</i>, <i>679</i>, 133201. <a href=\"https://doi.org/10.1016/j.neucom.2026.133201\">https://doi.org/10.1016/j.neucom.2026.133201</a>","ieee":"T. Markmann, M. Straat, S. Peitz, and B. Hammer, “Fourier neural operators as data-driven surrogates for two- and three-dimensional Rayleigh–Bénard convection,” <i>Neurocomputing</i>, vol. 679, p. 133201, 2026, doi: <a href=\"https://doi.org/10.1016/j.neucom.2026.133201\">10.1016/j.neucom.2026.133201</a>.","short":"T. Markmann, M. Straat, S. Peitz, B. Hammer, Neurocomputing 679 (2026) 133201.","chicago":"Markmann, Thorben, Michiel Straat, Sebastian Peitz, and Barbara Hammer. “Fourier Neural Operators as Data-Driven Surrogates for Two- and Three-Dimensional Rayleigh–Bénard Convection.” <i>Neurocomputing</i> 679 (2026): 133201. <a href=\"https://doi.org/10.1016/j.neucom.2026.133201\">https://doi.org/10.1016/j.neucom.2026.133201</a>."}},{"user_id":"47427","editor":[{"full_name":"Sukhatme, Gaurav","first_name":"Gaurav","last_name":"Sukhatme"},{"full_name":"Lindemann, Lars","first_name":"Lars","last_name":"Lindemann"},{"full_name":"Tu, Stephen","first_name":"Stephen","last_name":"Tu"},{"full_name":"Wierman, Adam","last_name":"Wierman","first_name":"Adam"},{"first_name":"Nikolay","last_name":"Atanasov","full_name":"Atanasov, Nikolay"}],"volume":331,"page":"1601–1619","_id":"67299","publisher":"PMLR","status":"public","oa":"1","citation":{"mla":"Harder, Hans, et al. “Efficient Probabilistic Surrogate Modeling Techniques for Partially-Observed Large-Scale Dynamical Systems.” <i>Proceedings of The 8th Annual Learning for Dynamics and Control Conference</i>, edited by Gaurav Sukhatme et al., vol. 331, PMLR, 2026, pp. 1601–1619, doi:<a href=\"https://doi.org/10.48550/arXiv.2511.04641\">10.48550/arXiv.2511.04641</a>.","ama":"Harder H, Vishwasrao A, Guastoni L, Vinuesa R, Peitz S. Efficient probabilistic surrogate modeling techniques for partially-observed large-scale dynamical systems. In: Sukhatme G, Lindemann L, Tu S, Wierman A, Atanasov N, eds. <i>Proceedings of The 8th Annual Learning for Dynamics and Control Conference</i>. Vol 331. Proceedings of Machine Learning Research. PMLR; 2026:1601–1619. doi:<a href=\"https://doi.org/10.48550/arXiv.2511.04641\">10.48550/arXiv.2511.04641</a>","bibtex":"@inproceedings{Harder_Vishwasrao_Guastoni_Vinuesa_Peitz_2026, series={Proceedings of Machine Learning Research}, title={Efficient probabilistic surrogate modeling techniques for partially-observed large-scale dynamical systems}, volume={331}, DOI={<a href=\"https://doi.org/10.48550/arXiv.2511.04641\">10.48550/arXiv.2511.04641</a>}, booktitle={Proceedings of The 8th Annual Learning for Dynamics and Control Conference}, publisher={PMLR}, author={Harder, Hans and Vishwasrao, Abhijeet and Guastoni, Luca and Vinuesa, Ricardo and Peitz, Sebastian}, editor={Sukhatme, Gaurav and Lindemann, Lars and Tu, Stephen and Wierman, Adam and Atanasov, Nikolay}, year={2026}, pages={1601–1619}, collection={Proceedings of Machine Learning Research} }","apa":"Harder, H., Vishwasrao, A., Guastoni, L., Vinuesa, R., &#38; Peitz, S. (2026). Efficient probabilistic surrogate modeling techniques for partially-observed large-scale dynamical systems. In G. Sukhatme, L. Lindemann, S. Tu, A. Wierman, &#38; N. Atanasov (Eds.), <i>Proceedings of The 8th Annual Learning for Dynamics and Control Conference</i> (Vol. 331, pp. 1601–1619). PMLR. <a href=\"https://doi.org/10.48550/arXiv.2511.04641\">https://doi.org/10.48550/arXiv.2511.04641</a>","ieee":"H. Harder, A. Vishwasrao, L. Guastoni, R. Vinuesa, and S. Peitz, “Efficient probabilistic surrogate modeling techniques for partially-observed large-scale dynamical systems,” in <i>Proceedings of The 8th Annual Learning for Dynamics and Control Conference</i>, 2026, vol. 331, pp. 1601–1619, doi: <a href=\"https://doi.org/10.48550/arXiv.2511.04641\">10.48550/arXiv.2511.04641</a>.","short":"H. Harder, A. Vishwasrao, L. Guastoni, R. Vinuesa, S. Peitz, in: G. Sukhatme, L. Lindemann, S. Tu, A. Wierman, N. Atanasov (Eds.), Proceedings of The 8th Annual Learning for Dynamics and Control Conference, PMLR, 2026, pp. 1601–1619.","chicago":"Harder, Hans, Abhijeet Vishwasrao, Luca Guastoni, Ricardo Vinuesa, and Sebastian Peitz. “Efficient Probabilistic Surrogate Modeling Techniques for Partially-Observed Large-Scale Dynamical Systems.” In <i>Proceedings of The 8th Annual Learning for Dynamics and Control Conference</i>, edited by Gaurav Sukhatme, Lars Lindemann, Stephen Tu, Adam Wierman, and Nikolay Atanasov, 331:1601–1619. Proceedings of Machine Learning Research. PMLR, 2026. <a href=\"https://doi.org/10.48550/arXiv.2511.04641\">https://doi.org/10.48550/arXiv.2511.04641</a>."},"doi":"10.48550/arXiv.2511.04641","main_file_link":[{"open_access":"1","url":"https://openreview.net/pdf?id=Z9srtyqVLE"}],"series_title":"Proceedings of Machine Learning Research","language":[{"iso":"eng"}],"date_updated":"2026-10-01T11:52:48Z","intvolume":"       331","year":"2026","title":"Efficient probabilistic surrogate modeling techniques for partially-observed large-scale dynamical systems","author":[{"id":"98879","full_name":"Harder, Hans","last_name":"Harder","first_name":"Hans"},{"full_name":"Vishwasrao, Abhijeet","first_name":"Abhijeet","last_name":"Vishwasrao"},{"last_name":"Guastoni","first_name":"Luca","full_name":"Guastoni, Luca"},{"first_name":"Ricardo","last_name":"Vinuesa","full_name":"Vinuesa, Ricardo"},{"full_name":"Peitz, Sebastian","first_name":"Sebastian","orcid":"0000-0002-3389-793X","last_name":"Peitz","id":"47427"}],"type":"conference","keyword":["own","own-conference","erc"],"department":[{"_id":"655"}],"date_created":"2026-10-01T11:51:57Z","publication":"Proceedings of The 8th Annual Learning for Dynamics and Control Conference"},{"_id":"67303","language":[{"iso":"eng"}],"page":"652-668","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2606.19521"}],"user_id":"47427","doi":"10.1007/978-3-032-37667-1_37","author":[{"id":"97916","full_name":"Amakor, Augustina Chidinma","last_name":"Amakor","first_name":"Augustina Chidinma"},{"first_name":"Konstantin","last_name":"Sonntag","orcid":"https://orcid.org/0000-0003-3384-3496","full_name":"Sonntag, Konstantin","id":"56399"},{"first_name":"Sebastian","orcid":"0000-0002-3389-793X","last_name":"Peitz","full_name":"Peitz, Sebastian","id":"47427"}],"year":"2026","status":"public","title":"Interactive Pareto navigation for deep multi-task learning","date_updated":"2026-10-01T11:58:43Z","date_created":"2026-10-01T11:56:15Z","department":[{"_id":"655"}],"oa":"1","keyword":["own","own-conference"],"type":"conference","citation":{"ama":"Amakor AC, Sonntag K, Peitz S. Interactive Pareto navigation for deep multi-task learning. In: <i>European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)</i>. ; 2026:652-668. doi:<a href=\"https://doi.org/10.1007/978-3-032-37667-1_37\">10.1007/978-3-032-37667-1_37</a>","bibtex":"@inproceedings{Amakor_Sonntag_Peitz_2026, title={Interactive Pareto navigation for deep multi-task learning}, DOI={<a href=\"https://doi.org/10.1007/978-3-032-37667-1_37\">10.1007/978-3-032-37667-1_37</a>}, booktitle={European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)}, author={Amakor, Augustina Chidinma and Sonntag, Konstantin and Peitz, Sebastian}, year={2026}, pages={652–668} }","mla":"Amakor, Augustina Chidinma, et al. “Interactive Pareto Navigation for Deep Multi-Task Learning.” <i>European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)</i>, 2026, pp. 652–68, doi:<a href=\"https://doi.org/10.1007/978-3-032-37667-1_37\">10.1007/978-3-032-37667-1_37</a>.","chicago":"Amakor, Augustina Chidinma, Konstantin Sonntag, and Sebastian Peitz. “Interactive Pareto Navigation for Deep Multi-Task Learning.” In <i>European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)</i>, 652–68, 2026. <a href=\"https://doi.org/10.1007/978-3-032-37667-1_37\">https://doi.org/10.1007/978-3-032-37667-1_37</a>.","short":"A.C. Amakor, K. Sonntag, S. Peitz, in: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 2026, pp. 652–668.","apa":"Amakor, A. C., Sonntag, K., &#38; Peitz, S. (2026). Interactive Pareto navigation for deep multi-task learning. <i>European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)</i>, 652–668. <a href=\"https://doi.org/10.1007/978-3-032-37667-1_37\">https://doi.org/10.1007/978-3-032-37667-1_37</a>","ieee":"A. C. Amakor, K. Sonntag, and S. Peitz, “Interactive Pareto navigation for deep multi-task learning,” in <i>European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)</i>, 2026, pp. 652–668, doi: <a href=\"https://doi.org/10.1007/978-3-032-37667-1_37\">10.1007/978-3-032-37667-1_37</a>."},"publication":"European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)"},{"date_created":"2026-10-01T11:59:35Z","type":"conference","keyword":["own","own-conference","erc"],"oa":"1","department":[{"_id":"655"}],"publication":"International Conference on Machine Learning (ICML)","citation":{"apa":"Becktepe, J., Franz, A., Thuerey, N., &#38; Peitz, S. (2026). Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control. <i>International Conference on Machine Learning (ICML)</i>. <a href=\"https://doi.org/10.48550/arXiv.2601.15015\">https://doi.org/10.48550/arXiv.2601.15015</a>","ieee":"J. Becktepe, A. Franz, N. Thuerey, and S. Peitz, “Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control,” 2026, doi: <a href=\"https://doi.org/10.48550/arXiv.2601.15015\">10.48550/arXiv.2601.15015</a>.","short":"J. Becktepe, A. Franz, N. Thuerey, S. Peitz, in: International Conference on Machine Learning (ICML), 2026.","chicago":"Becktepe, Jannis, Aleksandra Franz, Nils Thuerey, and Sebastian Peitz. “Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control.” In <i>International Conference on Machine Learning (ICML)</i>, 2026. <a href=\"https://doi.org/10.48550/arXiv.2601.15015\">https://doi.org/10.48550/arXiv.2601.15015</a>.","mla":"Becktepe, Jannis, et al. “Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control.” <i>International Conference on Machine Learning (ICML)</i>, 2026, doi:<a href=\"https://doi.org/10.48550/arXiv.2601.15015\">10.48550/arXiv.2601.15015</a>.","ama":"Becktepe J, Franz A, Thuerey N, Peitz S. Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control. In: <i>International Conference on Machine Learning (ICML)</i>. ; 2026. doi:<a href=\"https://doi.org/10.48550/arXiv.2601.15015\">10.48550/arXiv.2601.15015</a>","bibtex":"@inproceedings{Becktepe_Franz_Thuerey_Peitz_2026, title={Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control}, DOI={<a href=\"https://doi.org/10.48550/arXiv.2601.15015\">10.48550/arXiv.2601.15015</a>}, booktitle={International Conference on Machine Learning (ICML)}, author={Becktepe, Jannis and Franz, Aleksandra and Thuerey, Nils and Peitz, Sebastian}, year={2026} }"},"main_file_link":[{"url":"https://safe-autonomous-systems.github.io/fluidgym/","open_access":"1"}],"_id":"67305","language":[{"iso":"eng"}],"doi":"10.48550/arXiv.2601.15015","user_id":"47427","year":"2026","title":"Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control","status":"public","author":[{"last_name":"Becktepe","first_name":"Jannis","full_name":"Becktepe, Jannis"},{"full_name":"Franz, Aleksandra","first_name":"Aleksandra","last_name":"Franz"},{"last_name":"Thuerey","first_name":"Nils","full_name":"Thuerey, Nils"},{"full_name":"Peitz, Sebastian","last_name":"Peitz","orcid":"0000-0002-3389-793X","first_name":"Sebastian","id":"47427"}],"date_updated":"2026-10-01T12:00:51Z"},{"date_updated":"2026-10-01T11:54:23Z","year":"2026","title":"Evaluating Physics-Based, Hybrid, and Data-Driven Models for Rubber-Metal Bushings","status":"public","author":[{"id":"43991","first_name":"Meike Claudia","orcid":"0009-0009-9767-7168","last_name":"Wohlleben","full_name":"Wohlleben, Meike Claudia"},{"id":"22109","full_name":"Schütte, Jan","orcid":"0000-0001-9025-9742","first_name":"Jan","last_name":"Schütte"},{"full_name":"Berkemeier, Manuel","last_name":"Berkemeier","first_name":"Manuel"},{"last_name":"Sextro","first_name":"Walter","full_name":"Sextro, Walter","id":"21220"},{"full_name":"Peitz, Sebastian","orcid":"0000-0002-3389-793X","last_name":"Peitz","first_name":"Sebastian","id":"47427"}],"doi":"10.1007/s11044-026-10146-9","user_id":"47427","_id":"67301","language":[{"iso":"eng"}],"publication":"Multibody System Dynamics","citation":{"apa":"Wohlleben, M. C., Schütte, J., Berkemeier, M., Sextro, W., &#38; Peitz, S. (2026). Evaluating Physics-Based, Hybrid, and Data-Driven Models for Rubber-Metal Bushings. <i>Multibody System Dynamics</i>. <a href=\"https://doi.org/10.1007/s11044-026-10146-9\">https://doi.org/10.1007/s11044-026-10146-9</a>","ieee":"M. C. Wohlleben, J. Schütte, M. Berkemeier, W. Sextro, and S. Peitz, “Evaluating Physics-Based, Hybrid, and Data-Driven Models for Rubber-Metal Bushings,” <i>Multibody System Dynamics</i>, 2026, doi: <a href=\"https://doi.org/10.1007/s11044-026-10146-9\">10.1007/s11044-026-10146-9</a>.","chicago":"Wohlleben, Meike Claudia, Jan Schütte, Manuel Berkemeier, Walter Sextro, and Sebastian Peitz. “Evaluating Physics-Based, Hybrid, and Data-Driven Models for Rubber-Metal Bushings.” <i>Multibody System Dynamics</i>, 2026. <a href=\"https://doi.org/10.1007/s11044-026-10146-9\">https://doi.org/10.1007/s11044-026-10146-9</a>.","short":"M.C. Wohlleben, J. Schütte, M. Berkemeier, W. Sextro, S. Peitz, Multibody System Dynamics (2026).","mla":"Wohlleben, Meike Claudia, et al. “Evaluating Physics-Based, Hybrid, and Data-Driven Models for Rubber-Metal Bushings.” <i>Multibody System Dynamics</i>, 2026, doi:<a href=\"https://doi.org/10.1007/s11044-026-10146-9\">10.1007/s11044-026-10146-9</a>.","ama":"Wohlleben MC, Schütte J, Berkemeier M, Sextro W, Peitz S. Evaluating Physics-Based, Hybrid, and Data-Driven Models for Rubber-Metal Bushings. <i>Multibody System Dynamics</i>. Published online 2026. doi:<a href=\"https://doi.org/10.1007/s11044-026-10146-9\">10.1007/s11044-026-10146-9</a>","bibtex":"@article{Wohlleben_Schütte_Berkemeier_Sextro_Peitz_2026, title={Evaluating Physics-Based, Hybrid, and Data-Driven Models for Rubber-Metal Bushings}, DOI={<a href=\"https://doi.org/10.1007/s11044-026-10146-9\">10.1007/s11044-026-10146-9</a>}, journal={Multibody System Dynamics}, author={Wohlleben, Meike Claudia and Schütte, Jan and Berkemeier, Manuel and Sextro, Walter and Peitz, Sebastian}, year={2026} }"},"keyword":["own","own-journal"],"type":"journal_article","department":[{"_id":"655"}],"date_created":"2026-10-01T11:53:54Z"},{"department":[{"_id":"655"}],"oa":"1","type":"preprint","keyword":["own","own-preprint","erc"],"date_created":"2026-10-01T11:50:28Z","citation":{"mla":"Mugisho Zagabe, Christian, and Sebastian Peitz. “Automatic Feature Identification in Least-Squares Policy Iteration Using the Koopman Operator Framework.” <i>ArXiv:2603.26464</i>, 2026.","ama":"Mugisho Zagabe C, Peitz S. Automatic feature identification in least-squares policy iteration using the Koopman operator framework. <i>arXiv:260326464</i>. Published online 2026.","bibtex":"@article{Mugisho Zagabe_Peitz_2026, title={Automatic feature identification in least-squares policy iteration using the Koopman operator framework}, journal={arXiv:2603.26464}, author={Mugisho Zagabe, Christian and Peitz, Sebastian}, year={2026} }","apa":"Mugisho Zagabe, C., &#38; Peitz, S. (2026). Automatic feature identification in least-squares policy iteration using the Koopman operator framework. In <i>arXiv:2603.26464</i>.","ieee":"C. Mugisho Zagabe and S. Peitz, “Automatic feature identification in least-squares policy iteration using the Koopman operator framework,” <i>arXiv:2603.26464</i>. 2026.","chicago":"Mugisho Zagabe, Christian, and Sebastian Peitz. “Automatic Feature Identification in Least-Squares Policy Iteration Using the Koopman Operator Framework.” <i>ArXiv:2603.26464</i>, 2026.","short":"C. Mugisho Zagabe, S. Peitz, ArXiv:2603.26464 (2026)."},"publication":"arXiv:2603.26464","user_id":"47427","_id":"67298","language":[{"iso":"eng"}],"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2603.26464"}],"date_updated":"2026-10-01T11:51:41Z","author":[{"last_name":"Mugisho Zagabe","first_name":"Christian","full_name":"Mugisho Zagabe, Christian"},{"id":"47427","full_name":"Peitz, Sebastian","last_name":"Peitz","first_name":"Sebastian","orcid":"0000-0002-3389-793X"}],"year":"2026","status":"public","title":"Automatic feature identification in least-squares policy iteration using the Koopman operator framework"},{"user_id":"47427","_id":"67304","language":[{"iso":"eng"}],"main_file_link":[{"url":"https://arxiv.org/abs/2603.28074","open_access":"1"}],"date_updated":"2026-10-01T11:59:27Z","author":[{"full_name":"Plotzki, Tim","first_name":"Tim","last_name":"Plotzki"},{"full_name":"Peitz, Sebastian","last_name":"Peitz","orcid":"0000-0002-3389-793X","first_name":"Sebastian","id":"47427"}],"year":"2026","status":"public","title":"Koopman-based surrogate modeling for reinforcement-learning-control of Rayleigh-Benard convection","department":[{"_id":"655"}],"oa":"1","type":"preprint","keyword":["own","own-preprint","erc"],"date_created":"2026-10-01T11:59:03Z","citation":{"chicago":"Plotzki, Tim, and Sebastian Peitz. “Koopman-Based Surrogate Modeling for Reinforcement-Learning-Control of Rayleigh-Benard Convection.” <i>ArXiv:2603.28074</i>, 2026.","short":"T. Plotzki, S. Peitz, ArXiv:2603.28074 (2026).","ieee":"T. Plotzki and S. Peitz, “Koopman-based surrogate modeling for reinforcement-learning-control of Rayleigh-Benard convection,” <i>arXiv:2603.28074</i>. 2026.","apa":"Plotzki, T., &#38; Peitz, S. (2026). Koopman-based surrogate modeling for reinforcement-learning-control of Rayleigh-Benard convection. In <i>arXiv:2603.28074</i>.","bibtex":"@article{Plotzki_Peitz_2026, title={Koopman-based surrogate modeling for reinforcement-learning-control of Rayleigh-Benard convection}, journal={arXiv:2603.28074}, author={Plotzki, Tim and Peitz, Sebastian}, year={2026} }","ama":"Plotzki T, Peitz S. Koopman-based surrogate modeling for reinforcement-learning-control of Rayleigh-Benard convection. <i>arXiv:260328074</i>. Published online 2026.","mla":"Plotzki, Tim, and Sebastian Peitz. “Koopman-Based Surrogate Modeling for Reinforcement-Learning-Control of Rayleigh-Benard Convection.” <i>ArXiv:2603.28074</i>, 2026."},"publication":"arXiv:2603.28074"},{"department":[{"_id":"35"},{"_id":"2"},{"_id":"307"}],"type":"journal_article","date_created":"2025-12-03T17:09:28Z","abstract":[{"text":"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.","lang":"eng"}],"publication":"Advanced Functional Materials","doi":"10.1002/adfm.202511190","language":[{"iso":"eng"}],"main_file_link":[{"open_access":"1"}],"article_number":"e11190","date_updated":"2026-10-01T14:06:35Z","publication_status":"published","publication_identifier":{"issn":["1616-301X","1616-3028"]},"author":[{"last_name":"Zhao","first_name":"Zhenyu","full_name":"Zhao, Zhenyu"},{"full_name":"Weinberger, Christian","last_name":"Weinberger","first_name":"Christian","id":"11848"},{"full_name":"Steube, Jakob","orcid":"0000-0003-3178-4429","last_name":"Steube","first_name":"Jakob","id":"40342"},{"orcid":"0000-0002-9294-6076","first_name":"Matthias","last_name":"Bauer","full_name":"Bauer, Matthias","id":"47241"},{"full_name":"Brehm, Martin","first_name":"Martin","last_name":"Brehm","id":"100167"},{"id":"23547","full_name":"Tiemann, Michael","orcid":"0000-0003-1711-2722","first_name":"Michael","last_name":"Tiemann"}],"year":"2026","title":"Fast‐Responding O2 Gas Sensor Based on Luminescent Europium Metal‐Organic Frameworks (MOF‐76)","oa":"1","quality_controlled":"1","citation":{"bibtex":"@article{Zhao_Weinberger_Steube_Bauer_Brehm_Tiemann_2026, title={Fast‐Responding O2 Gas Sensor Based on Luminescent Europium Metal‐Organic Frameworks (MOF‐76)}, DOI={<a href=\"https://doi.org/10.1002/adfm.202511190\">10.1002/adfm.202511190</a>}, number={e11190}, journal={Advanced Functional Materials}, publisher={Wiley}, author={Zhao, Zhenyu and Weinberger, Christian and Steube, Jakob and Bauer, Matthias and Brehm, Martin and Tiemann, Michael}, year={2026} }","ama":"Zhao Z, Weinberger C, Steube J, Bauer M, Brehm M, Tiemann M. Fast‐Responding O2 Gas Sensor Based on Luminescent Europium Metal‐Organic Frameworks (MOF‐76). <i>Advanced Functional Materials</i>. Published online 2026. doi:<a href=\"https://doi.org/10.1002/adfm.202511190\">10.1002/adfm.202511190</a>","mla":"Zhao, Zhenyu, et al. “Fast‐Responding O2 Gas Sensor Based on Luminescent Europium Metal‐Organic Frameworks (MOF‐76).” <i>Advanced Functional Materials</i>, e11190, Wiley, 2026, doi:<a href=\"https://doi.org/10.1002/adfm.202511190\">10.1002/adfm.202511190</a>.","chicago":"Zhao, Zhenyu, Christian Weinberger, Jakob Steube, Matthias Bauer, Martin Brehm, and Michael Tiemann. “Fast‐Responding O2 Gas Sensor Based on Luminescent Europium Metal‐Organic Frameworks (MOF‐76).” <i>Advanced Functional Materials</i>, 2026. <a href=\"https://doi.org/10.1002/adfm.202511190\">https://doi.org/10.1002/adfm.202511190</a>.","short":"Z. Zhao, C. Weinberger, J. Steube, M. Bauer, M. Brehm, M. Tiemann, Advanced Functional Materials (2026).","ieee":"Z. Zhao, C. Weinberger, J. Steube, M. Bauer, M. Brehm, and M. Tiemann, “Fast‐Responding O2 Gas Sensor Based on Luminescent Europium Metal‐Organic Frameworks (MOF‐76),” <i>Advanced Functional Materials</i>, Art. no. e11190, 2026, doi: <a href=\"https://doi.org/10.1002/adfm.202511190\">10.1002/adfm.202511190</a>.","apa":"Zhao, Z., Weinberger, C., Steube, J., Bauer, M., Brehm, M., &#38; Tiemann, M. (2026). Fast‐Responding O2 Gas Sensor Based on Luminescent Europium Metal‐Organic Frameworks (MOF‐76). <i>Advanced Functional Materials</i>, Article e11190. <a href=\"https://doi.org/10.1002/adfm.202511190\">https://doi.org/10.1002/adfm.202511190</a>"},"user_id":"23547","_id":"62816","publisher":"Wiley","status":"public"},{"_id":"67339","user_id":"67234","status":"public","title":"LYRA: Belief-Driven Scalable Class Expression Learning in Description Logics","year":"2026","author":[{"first_name":"Amgad","last_name":"Abdulmaqsod","full_name":"Abdulmaqsod, Amgad"},{"full_name":"Mahmood, Yasir","first_name":"Yasir","last_name":"Mahmood"},{"full_name":"Ngonga Ngomo, Axel-Cyrille","last_name":"Ngonga Ngomo","first_name":"Axel-Cyrille"},{"full_name":"Sherif, Mohamed Ahmed","last_name":"Sherif","first_name":"Mohamed Ahmed"}],"date_updated":"2026-10-02T08:00:46Z","place":"Bari, Italy","date_created":"2026-10-02T07:50:24Z","keyword":["amgad dice enexa fairomics mahmood ngonga sailproject sherif simba whale"],"type":"conference","publication":"The Semantic Web – ISWC 2026","citation":{"short":"A. Abdulmaqsod, Y. Mahmood, A.-C. Ngonga Ngomo, M.A. Sherif, in: The Semantic Web – ISWC 2026, Bari, Italy, 2026.","chicago":"Abdulmaqsod, Amgad, Yasir Mahmood, Axel-Cyrille Ngonga Ngomo, and Mohamed Ahmed Sherif. “LYRA: Belief-Driven Scalable Class Expression Learning in Description Logics.” In <i>The Semantic Web – ISWC 2026</i>. Bari, Italy, 2026.","ieee":"A. Abdulmaqsod, Y. Mahmood, A.-C. Ngonga Ngomo, and M. A. Sherif, “LYRA: Belief-Driven Scalable Class Expression Learning in Description Logics,” 2026.","apa":"Abdulmaqsod, A., Mahmood, Y., Ngonga Ngomo, A.-C., &#38; Sherif, M. A. (2026). LYRA: Belief-Driven Scalable Class Expression Learning in Description Logics. <i>The Semantic Web – ISWC 2026</i>.","bibtex":"@inproceedings{Abdulmaqsod_Mahmood_Ngonga Ngomo_Sherif_2026, place={Bari, Italy}, title={LYRA: Belief-Driven Scalable Class Expression Learning in Description Logics}, booktitle={The Semantic Web – ISWC 2026}, author={Abdulmaqsod, Amgad and Mahmood, Yasir and Ngonga Ngomo, Axel-Cyrille and Sherif, Mohamed Ahmed}, year={2026} }","ama":"Abdulmaqsod A, Mahmood Y, Ngonga Ngomo A-C, Sherif MA. LYRA: Belief-Driven Scalable Class Expression Learning in Description Logics. In: <i>The Semantic Web – ISWC 2026</i>. ; 2026.","mla":"Abdulmaqsod, Amgad, et al. “LYRA: Belief-Driven Scalable Class Expression Learning in Description Logics.” <i>The Semantic Web – ISWC 2026</i>, 2026."},"abstract":[{"text":"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.","lang":"eng"}]},{"date_updated":"2026-10-02T07:57:39Z","status":"public","year":"2026","title":"TIM: Tiered Iterative Knowledge Graph Matching","author":[{"full_name":"Becker, Alexander","first_name":"Alexander","last_name":"Becker"},{"id":"65716","full_name":"Ngonga Ngomo, Axel-Cyrille","last_name":"Ngonga Ngomo","first_name":"Axel-Cyrille"},{"id":"67234","full_name":"Sherif, Mohamed","first_name":"Mohamed","last_name":"Sherif","orcid":"https://orcid.org/0000-0002-9927-2203"}],"user_id":"67234","_id":"67344","publisher":"Springer Nature Switzerland","language":[{"iso":"eng"}],"publication":"The Semantic Web – 23rd European Semantic Web Conference, ESWC 2026, Dubrovnik , Croatia, May 10-14, 2026, Proceedings","citation":{"ieee":"A. Becker, A.-C. Ngonga Ngomo, and M. Sherif, “TIM: Tiered Iterative Knowledge Graph Matching,” 2026.","apa":"Becker, A., Ngonga Ngomo, A.-C., &#38; Sherif, M. (2026). TIM: Tiered Iterative Knowledge Graph Matching. <i>The Semantic Web – 23rd European Semantic Web Conference, ESWC 2026, Dubrovnik , Croatia, May 10-14, 2026, Proceedings</i>.","short":"A. Becker, A.-C. Ngonga Ngomo, M. Sherif, in: The Semantic Web – 23rd European Semantic Web Conference, ESWC 2026, Dubrovnik , Croatia, May 10-14, 2026, Proceedings, Springer Nature Switzerland, 2026.","chicago":"Becker, Alexander, Axel-Cyrille Ngonga Ngomo, and Mohamed Sherif. “TIM: Tiered Iterative Knowledge Graph Matching.” In <i>The Semantic Web – 23rd European Semantic Web Conference, ESWC 2026, Dubrovnik , Croatia, May 10-14, 2026, Proceedings</i>. Springer Nature Switzerland, 2026.","mla":"Becker, Alexander, et al. “TIM: Tiered Iterative Knowledge Graph Matching.” <i>The Semantic Web – 23rd European Semantic Web Conference, ESWC 2026, Dubrovnik , Croatia, May 10-14, 2026, Proceedings</i>, Springer Nature Switzerland, 2026.","bibtex":"@inproceedings{Becker_Ngonga Ngomo_Sherif_2026, title={TIM: Tiered Iterative Knowledge Graph Matching}, booktitle={The Semantic Web – 23rd European Semantic Web Conference, ESWC 2026, Dubrovnik , Croatia, May 10-14, 2026, Proceedings}, publisher={Springer Nature Switzerland}, author={Becker, Alexander and Ngonga Ngomo, Axel-Cyrille and Sherif, Mohamed}, year={2026} }","ama":"Becker A, Ngonga Ngomo A-C, Sherif M. TIM: Tiered Iterative Knowledge Graph Matching. In: <i>The Semantic Web – 23rd European Semantic Web Conference, ESWC 2026, Dubrovnik , Croatia, May 10-14, 2026, Proceedings</i>. Springer Nature Switzerland; 2026."},"keyword":["becker dice enexa kiowl ngonga sailproject sherif trr318_inf whale"],"type":"conference","date_created":"2026-10-02T07:57:09Z"},{"user_id":"67234","language":[{"iso":"eng"}],"_id":"67341","date_updated":"2026-10-02T08:02:17Z","author":[{"last_name":"Ekinci Birol","first_name":"Duygu","full_name":"Ekinci Birol, Duygu"},{"full_name":"KOUAGOU, N'Dah Jean","last_name":"KOUAGOU","first_name":"N'Dah Jean","id":"87189"},{"id":"67234","full_name":"Sherif, Mohamed","first_name":"Mohamed","orcid":"https://orcid.org/0000-0002-9927-2203","last_name":"Sherif"},{"full_name":"Ngonga Ngomo, Axel-Cyrille","last_name":"Ngonga Ngomo","first_name":"Axel-Cyrille","id":"65716"}],"status":"public","title":"ATLAS: Adaptive Attribute-Aware Post-Hoc Alignment of Knowledge Graph Embeddings","year":"2026","keyword":["dice duygu fairomics kouagou ngonga sail sherif trr318 whale"],"type":"conference","date_created":"2026-10-02T07:51:10Z","citation":{"mla":"Ekinci Birol, Duygu, et al. “ATLAS: Adaptive Attribute-Aware Post-Hoc Alignment of Knowledge Graph Embeddings.” <i>IEEE International Conference on Data Mining (ICDM) 2026</i>, 2026.","bibtex":"@inproceedings{Ekinci Birol_KOUAGOU_Sherif_Ngonga Ngomo_2026, title={ATLAS: Adaptive Attribute-Aware Post-Hoc Alignment of Knowledge Graph Embeddings}, booktitle={IEEE International Conference on Data Mining (ICDM) 2026}, author={Ekinci Birol, Duygu and KOUAGOU, N’Dah Jean and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}, year={2026} }","ama":"Ekinci Birol D, KOUAGOU NJ, Sherif M, Ngonga Ngomo A-C. 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These developments increase exposure to a range of disruptions, from technical failures to cyberattacks, and demand a shift towards resilience-by-design. This study addresses the early integration of resilience into the automotive design process by proposing a structured method for identifying gaps and eliciting resilience requirements. Building upon the concept of resilience scenarios, the approach extends traditional hazard and threat analyses as defined in ISO 26262, ISO 21448 and ISO/SAE 21434. Using a structured, graph-based modeling method, these scenarios enable the anticipation of functional degradation and its impact on driving scenarios. The methodology helps developers to specify resilience requirements at an early stage, enabling the integration of resilience properties throughout the system lifecycle. Its practical applicability is demonstrated through an example in the field of automotive cybersecurity. This study advances the field of resilience engineering by providing a concrete approach for operationalizing resilience within automotive systems engineering.</jats:p>"}],"article_number":"51","language":[{"iso":"eng"}],"doi":"10.3390/fi18010051","year":"2026","title":"Resilience-by-Design: Extracting Resilience Requirements Using the Resilience Graph in the Automotive Concept Phase","author":[{"last_name":"Mpidi Bita","first_name":"Isaac","full_name":"Mpidi Bita, Isaac"},{"full_name":"Ugur, Elif","last_name":"Ugur","first_name":"Elif"},{"full_name":"Hovemann, Aschot","first_name":"Aschot","last_name":"Hovemann"},{"id":"16190","full_name":"Dumitrescu, Roman","last_name":"Dumitrescu","first_name":"Roman"}],"publication_identifier":{"issn":["1999-5903"]},"publication_status":"published","date_updated":"2026-10-02T13:45:32Z","intvolume":"        18","citation":{"mla":"Mpidi Bita, Isaac, et al. “Resilience-by-Design: Extracting Resilience Requirements Using the Resilience Graph in the Automotive Concept Phase.” <i>Future Internet</i>, vol. 18, no. 1, 51, MDPI AG, 2026, doi:<a href=\"https://doi.org/10.3390/fi18010051\">10.3390/fi18010051</a>.","bibtex":"@article{Mpidi Bita_Ugur_Hovemann_Dumitrescu_2026, title={Resilience-by-Design: Extracting Resilience Requirements Using the Resilience Graph in the Automotive Concept Phase}, volume={18}, DOI={<a href=\"https://doi.org/10.3390/fi18010051\">10.3390/fi18010051</a>}, number={151}, journal={Future Internet}, publisher={MDPI AG}, author={Mpidi Bita, Isaac and Ugur, Elif and Hovemann, Aschot and Dumitrescu, Roman}, year={2026} }","ama":"Mpidi Bita I, Ugur E, Hovemann A, Dumitrescu R. 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