{"doi":"10.48550/arXiv.2511.04641","main_file_link":[{"open_access":"1","url":"https://openreview.net/pdf?id=Z9srtyqVLE"}],"language":[{"iso":"eng"}],"series_title":"Proceedings of Machine Learning Research","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":[{"first_name":"Hans","last_name":"Harder","full_name":"Harder, Hans","id":"98879"},{"last_name":"Vishwasrao","first_name":"Abhijeet","full_name":"Vishwasrao, Abhijeet"},{"last_name":"Guastoni","first_name":"Luca","full_name":"Guastoni, Luca"},{"full_name":"Vinuesa, Ricardo","last_name":"Vinuesa","first_name":"Ricardo"},{"id":"47427","first_name":"Sebastian","orcid":"0000-0002-3389-793X","last_name":"Peitz","full_name":"Peitz, Sebastian"}],"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","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"},{"first_name":"Adam","last_name":"Wierman","full_name":"Wierman, Adam"},{"last_name":"Atanasov","first_name":"Nikolay","full_name":"Atanasov, Nikolay"}],"volume":331,"page":"1601–1619","_id":"67299","publisher":"PMLR","status":"public","oa":"1","citation":{"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>.","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>.","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} }","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>."}}