[{"language":[{"iso":"eng"}],"doi":"10.1007/978-3-032-04555-3_21","year":"2026","title":"Surrogate-Assisted Multi-objective Design of Complex Multibody Systems","author":[{"full_name":"Amakor, Augustina Chidinma","first_name":"Augustina Chidinma","last_name":"Amakor","id":"97916"},{"full_name":"Berkemeier, Manuel B.","last_name":"Berkemeier","first_name":"Manuel B."},{"id":"43991","full_name":"Wohlleben, Meike Claudia","first_name":"Meike Claudia","orcid":"0009-0009-9767-7168","last_name":"Wohlleben"},{"id":"21220","last_name":"Sextro","first_name":"Walter","full_name":"Sextro, Walter"},{"full_name":"Peitz, Sebastian","orcid":"0000-0002-3389-793X","last_name":"Peitz","first_name":"Sebastian","id":"47427"}],"publication_identifier":{"isbn":["978-3-032-04555-3"]},"date_updated":"2026-10-01T12:03:16Z","date_created":"2026-10-01T12:02:40Z","type":"conference","keyword":["own","own-conference"],"department":[{"_id":"655"}],"publication":"Artificial Neural Networks and Machine Learning – ICANN 2025","abstract":[{"text":"Optimizing large-scale multibody systems is a challenging task, particularly in the presence of multiple conflicting criteria. To prevent high simulation costs, surrogate models constructed from a small number of expensive model evaluations are very popular. However, it is difficult to ensure the optimality of the obtained solutions using a single pre-computed model. We present a back-and-forth approach between surrogate modeling and multi-objective optimization, and we compare different strategies for optimization, sampling, and surrogate modeling, to identify the most promising approach in terms of computational efficiency and solution quality.","lang":"eng"}],"page":"251–262","_id":"67308","publisher":"Springer Nature Switzerland","user_id":"47427","editor":[{"last_name":"Senn","first_name":"Walter","full_name":"Senn, Walter"},{"full_name":"Sanguineti, Marcello","first_name":"Marcello","last_name":"Sanguineti"},{"full_name":"Saudargiene, Ausra","last_name":"Saudargiene","first_name":"Ausra"},{"full_name":"Tetko, Igor V.","last_name":"Tetko","first_name":"Igor V."},{"full_name":"Villa, Alessandro E. P.","last_name":"Villa","first_name":"Alessandro E. P."},{"last_name":"Jirsa","first_name":"Viktor","full_name":"Jirsa, Viktor"},{"last_name":"Bengio","first_name":"Yoshua","full_name":"Bengio, Yoshua"}],"status":"public","place":"Cham","citation":{"mla":"Amakor, Augustina Chidinma, et al. “Surrogate-Assisted Multi-Objective Design of Complex Multibody Systems.” <i>Artificial Neural Networks and Machine Learning – ICANN 2025</i>, edited by Walter Senn et al., Springer Nature Switzerland, 2026, pp. 251–262, doi:<a href=\"https://doi.org/10.1007/978-3-032-04555-3_21\">10.1007/978-3-032-04555-3_21</a>.","bibtex":"@inproceedings{Amakor_Berkemeier_Wohlleben_Sextro_Peitz_2026, place={Cham}, title={Surrogate-Assisted Multi-objective Design of Complex Multibody Systems}, DOI={<a href=\"https://doi.org/10.1007/978-3-032-04555-3_21\">10.1007/978-3-032-04555-3_21</a>}, booktitle={Artificial Neural Networks and Machine Learning – ICANN 2025}, publisher={Springer Nature Switzerland}, author={Amakor, Augustina Chidinma and Berkemeier, Manuel B. and Wohlleben, Meike Claudia and Sextro, Walter 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={251–262} }","ama":"Amakor AC, Berkemeier MB, Wohlleben MC, Sextro W, Peitz S. Surrogate-Assisted Multi-objective Design of Complex Multibody Systems. In: Senn W, Sanguineti M, Saudargiene A, et al., eds. <i>Artificial Neural Networks and Machine Learning – ICANN 2025</i>. Springer Nature Switzerland; 2026:251–262. doi:<a href=\"https://doi.org/10.1007/978-3-032-04555-3_21\">10.1007/978-3-032-04555-3_21</a>","ieee":"A. C. Amakor, M. B. Berkemeier, M. C. Wohlleben, W. Sextro, and S. Peitz, “Surrogate-Assisted Multi-objective Design of Complex Multibody Systems,” in <i>Artificial Neural Networks and Machine Learning – ICANN 2025</i>, 2026, pp. 251–262, doi: <a href=\"https://doi.org/10.1007/978-3-032-04555-3_21\">10.1007/978-3-032-04555-3_21</a>.","apa":"Amakor, A. C., Berkemeier, M. B., Wohlleben, M. C., Sextro, W., &#38; Peitz, S. (2026). Surrogate-Assisted Multi-objective Design of Complex Multibody Systems. 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. 251–262). Springer Nature Switzerland. <a href=\"https://doi.org/10.1007/978-3-032-04555-3_21\">https://doi.org/10.1007/978-3-032-04555-3_21</a>","chicago":"Amakor, Augustina Chidinma, Manuel B. Berkemeier, Meike Claudia Wohlleben, Walter Sextro, and Sebastian Peitz. “Surrogate-Assisted Multi-Objective Design of Complex Multibody Systems.” 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, 251–262. Cham: Springer Nature Switzerland, 2026. <a href=\"https://doi.org/10.1007/978-3-032-04555-3_21\">https://doi.org/10.1007/978-3-032-04555-3_21</a>.","short":"A.C. Amakor, M.B. Berkemeier, M.C. Wohlleben, W. Sextro, 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. 251–262."}},{"citation":{"short":"S.S. Hotegni, S. Peitz, in: 14th International Conference on Learning Representations (ICLR), 2026.","chicago":"Hotegni, Sedjro Salomon, and Sebastian Peitz. “SPREAD: Sampling-Based Pareto Front Refinement via Efficient Adaptive Diffusion.” In <i>14th International Conference on Learning Representations (ICLR)</i>, 2026. <a href=\"https://doi.org/10.48550/arXiv.2509.21058\">https://doi.org/10.48550/arXiv.2509.21058</a>.","apa":"Hotegni, S. S., &#38; Peitz, S. (2026). SPREAD: Sampling-based Pareto front Refinement via Efficient Adaptive Diffusion. <i>14th International Conference on Learning Representations (ICLR)</i>. <a href=\"https://doi.org/10.48550/arXiv.2509.21058\">https://doi.org/10.48550/arXiv.2509.21058</a>","ieee":"S. S. Hotegni and S. Peitz, “SPREAD: Sampling-based Pareto front Refinement via Efficient Adaptive Diffusion,” 2026, doi: <a href=\"https://doi.org/10.48550/arXiv.2509.21058\">10.48550/arXiv.2509.21058</a>.","ama":"Hotegni SS, Peitz S. SPREAD: Sampling-based Pareto front Refinement via Efficient Adaptive Diffusion. In: <i>14th International Conference on Learning Representations (ICLR)</i>. ; 2026. doi:<a href=\"https://doi.org/10.48550/arXiv.2509.21058\">10.48550/arXiv.2509.21058</a>","bibtex":"@inproceedings{Hotegni_Peitz_2026, title={SPREAD: Sampling-based Pareto front Refinement via Efficient Adaptive Diffusion}, DOI={<a href=\"https://doi.org/10.48550/arXiv.2509.21058\">10.48550/arXiv.2509.21058</a>}, booktitle={14th International Conference on Learning Representations (ICLR)}, author={Hotegni, Sedjro Salomon and Peitz, Sebastian}, year={2026} }","mla":"Hotegni, Sedjro Salomon, and Sebastian Peitz. “SPREAD: Sampling-Based Pareto Front Refinement via Efficient Adaptive Diffusion.” <i>14th International Conference on Learning Representations (ICLR)</i>, 2026, doi:<a href=\"https://doi.org/10.48550/arXiv.2509.21058\">10.48550/arXiv.2509.21058</a>."},"publication":"14th International Conference on Learning Representations (ICLR)","department":[{"_id":"655"}],"oa":"1","type":"conference","keyword":["own","own-conference"],"date_created":"2026-10-01T12:01:30Z","date_updated":"2026-10-01T12:02:30Z","author":[{"last_name":"Hotegni","first_name":"Sedjro Salomon","full_name":"Hotegni, Sedjro Salomon","id":"97995"},{"id":"47427","full_name":"Peitz, Sebastian","last_name":"Peitz","orcid":"0000-0002-3389-793X","first_name":"Sebastian"}],"status":"public","title":"SPREAD: Sampling-based Pareto front Refinement via Efficient Adaptive Diffusion","year":"2026","user_id":"47427","doi":"10.48550/arXiv.2509.21058","_id":"67307","language":[{"iso":"eng"}],"main_file_link":[{"url":"https://openreview.net/pdf?id=4731mIqv89","open_access":"1"}]},{"publication":"arXiv:2606.30238","citation":{"short":"J. Stenner, H. Harder, S. Peitz, ArXiv:2606.30238 (2026).","chicago":"Stenner, Jan, Hans Harder, and Sebastian Peitz. “Sparse Sensor Placement in Multi-Agent Reinforcement Learning Control of Rayleigh-Bénard Convection.” <i>ArXiv:2606.30238</i>, 2026.","ieee":"J. Stenner, H. Harder, and S. Peitz, “Sparse Sensor Placement in Multi-Agent Reinforcement Learning Control of Rayleigh-Bénard Convection,” <i>arXiv:2606.30238</i>. 2026.","apa":"Stenner, J., Harder, H., &#38; Peitz, S. (2026). Sparse Sensor Placement in Multi-Agent Reinforcement Learning Control of Rayleigh-Bénard Convection. In <i>arXiv:2606.30238</i>.","bibtex":"@article{Stenner_Harder_Peitz_2026, title={Sparse Sensor Placement in Multi-Agent Reinforcement Learning Control of Rayleigh-Bénard Convection}, journal={arXiv:2606.30238}, author={Stenner, Jan and Harder, Hans and Peitz, Sebastian}, year={2026} }","ama":"Stenner J, Harder H, Peitz S. Sparse Sensor Placement in Multi-Agent Reinforcement Learning Control of Rayleigh-Bénard Convection. <i>arXiv:260630238</i>. Published online 2026.","mla":"Stenner, Jan, et al. “Sparse Sensor Placement in Multi-Agent Reinforcement Learning Control of Rayleigh-Bénard Convection.” <i>ArXiv:2606.30238</i>, 2026."},"keyword":["own","own-preprint","erc"],"type":"preprint","department":[{"_id":"655"}],"oa":"1","date_created":"2026-10-01T12:00:58Z","date_updated":"2026-10-01T12:01:23Z","year":"2026","title":"Sparse Sensor Placement in Multi-Agent Reinforcement Learning Control of Rayleigh-Bénard Convection","status":"public","author":[{"last_name":"Stenner","first_name":"Jan","full_name":"Stenner, Jan"},{"last_name":"Harder","first_name":"Hans","full_name":"Harder, Hans","id":"98879"},{"id":"47427","last_name":"Peitz","orcid":"0000-0002-3389-793X","first_name":"Sebastian","full_name":"Peitz, Sebastian"}],"user_id":"47427","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/2606.30238"}],"_id":"67306","language":[{"iso":"eng"}]},{"status":"public","year":"2026","title":"Toward an Energy-Optimized Operation of Data Centers Located in Wind Farms Using Reinforcement Learning","author":[{"first_name":"Jan","last_name":"Stenner","full_name":"Stenner, Jan","id":"65520"},{"full_name":"Kilian, Alexander","first_name":"Alexander","last_name":"Kilian"},{"id":"47427","orcid":"0000-0002-3389-793X","first_name":"Sebastian","last_name":"Peitz","full_name":"Peitz, Sebastian"},{"full_name":"de Meer, Hermann","first_name":"Hermann","last_name":"de Meer"}],"date_updated":"2026-10-01T12:03:35Z","language":[{"iso":"eng"}],"_id":"67309","user_id":"47427","publication":"arXiv:2606.30316","citation":{"short":"J. Stenner, A. Kilian, S. Peitz, H. de Meer, ArXiv:2606.30316 (2026).","chicago":"Stenner, Jan, Alexander Kilian, Sebastian Peitz, and Hermann de Meer. “Toward an Energy-Optimized Operation of Data Centers Located in Wind Farms Using Reinforcement Learning.” <i>ArXiv:2606.30316</i>, 2026.","apa":"Stenner, J., Kilian, A., Peitz, S., &#38; de Meer, H. (2026). Toward an Energy-Optimized Operation of Data Centers Located in Wind Farms Using Reinforcement Learning. In <i>arXiv:2606.30316</i>.","ieee":"J. Stenner, A. Kilian, S. Peitz, and H. de Meer, “Toward an Energy-Optimized Operation of Data Centers Located in Wind Farms Using Reinforcement Learning,” <i>arXiv:2606.30316</i>. 2026.","ama":"Stenner J, Kilian A, Peitz S, de Meer H. Toward an Energy-Optimized Operation of Data Centers Located in Wind Farms Using Reinforcement Learning. <i>arXiv:260630316</i>. Published online 2026.","bibtex":"@article{Stenner_Kilian_Peitz_de Meer_2026, title={Toward an Energy-Optimized Operation of Data Centers Located in Wind Farms Using Reinforcement Learning}, journal={arXiv:2606.30316}, author={Stenner, Jan and Kilian, Alexander and Peitz, Sebastian and de Meer, Hermann}, year={2026} }","mla":"Stenner, Jan, et al. “Toward an Energy-Optimized Operation of Data Centers Located in Wind Farms Using Reinforcement Learning.” <i>ArXiv:2606.30316</i>, 2026."},"date_created":"2026-10-01T12:03:21Z","type":"preprint","keyword":["own","own-preprint"],"department":[{"_id":"655"}]},{"doi":"10.1007/978-3-032-04558-4_35","language":[{"iso":"eng"}],"date_updated":"2026-10-01T11:53:46Z","publication_identifier":{"isbn":["978-3-032-04558-4"]},"author":[{"first_name":"Sedjro Salomon","last_name":"Hotegni","full_name":"Hotegni, Sedjro Salomon","id":"97995"},{"full_name":"Peitz, Sebastian","last_name":"Peitz","first_name":"Sebastian","orcid":"0000-0002-3389-793X","id":"47427"}],"title":"Enhancing Adversarial Robustness Through Multi-objective Representation Learning","year":"2026","department":[{"_id":"655"}],"keyword":["own","own-conference"],"type":"conference","date_created":"2026-10-01T11:53:11Z","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."}],"publication":"Artificial Neural Networks and Machine Learning – ICANN 2025","editor":[{"full_name":"Senn, Walter","last_name":"Senn","first_name":"Walter"},{"full_name":"Sanguineti, Marcello","first_name":"Marcello","last_name":"Sanguineti"},{"last_name":"Saudargiene","first_name":"Ausra","full_name":"Saudargiene, Ausra"},{"full_name":"Tetko, Igor V.","last_name":"Tetko","first_name":"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","publisher":"Springer Nature Switzerland","_id":"67300","page":"442–454","status":"public","place":"Cham","citation":{"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>","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} }","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>.","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>.","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.","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>","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>."}},{"department":[{"_id":"655"}],"keyword":["own","own-journal","erc"],"type":"journal_article","date_created":"2026-10-01T11:55:20Z","abstract":[{"lang":"eng","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."}],"citation":{"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>.","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>","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>.","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>.","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} }","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>"},"publication":"Neurocomputing","volume":679,"user_id":"47427","doi":"10.1016/j.neucom.2026.133201","language":[{"iso":"eng"}],"_id":"67302","page":"133201","intvolume":"       679","date_updated":"2026-10-01T11:56:04Z","publication_identifier":{"issn":["0925-2312"]},"author":[{"full_name":"Markmann, Thorben","last_name":"Markmann","first_name":"Thorben"},{"first_name":"Michiel","last_name":"Straat","full_name":"Straat, Michiel"},{"last_name":"Peitz","first_name":"Sebastian","orcid":"0000-0002-3389-793X","full_name":"Peitz, Sebastian","id":"47427"},{"first_name":"Barbara","last_name":"Hammer","full_name":"Hammer, Barbara"}],"year":"2026","title":"Fourier neural operators as data-driven surrogates for two- and three-dimensional Rayleigh–Bénard convection","status":"public"},{"oa":"1","citation":{"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} }","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>","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>.","short":"H. Harder, A. Vishwasrao, L. Guastoni, R. Vinuesa, S. Peitz, in: G. Sukhatme, L. Lindemann, S. Tu, A. Wierman, N. 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