{"status":"public","volume":283,"acknowledgement":"This work was supported in part by the ERC project ERC-2020-AdG 101020093.\r\n","oa_version":"Published Version","ec_funded":1,"OA_place":"publisher","article_processing_charge":"No","oa":1,"ddc":["000"],"author":[{"last_name":"Henzinger","full_name":"Henzinger, Thomas A","first_name":"Thomas A","orcid":"0000-0002-2985-7724","id":"40876CD8-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Kresse","full_name":"Kresse, Fabian","first_name":"Fabian","id":"faff3c84-23f6-11ef-9085-e5187b51c604"},{"first_name":"Kaushik","full_name":"Mallik, Kaushik","orcid":"0000-0001-9864-7475","id":"0834ff3c-6d72-11ec-94e0-b5b0a4fb8598","last_name":"Mallik"},{"last_name":"Yu","first_name":"Zhengqi","full_name":"Yu, Zhengqi","id":"20aa2ae8-f2f1-11ed-bbfa-8205053f1342"},{"last_name":"Zikelic","orcid":"0000-0002-4681-1699","full_name":"Zikelic, Dorde","first_name":"Dorde","id":"294AA7A6-F248-11E8-B48F-1D18A9856A87"}],"abstract":[{"lang":"eng","text":"We study the problem of predictive runtime monitoring of black-box dynamical systems with quantitative safety properties. The black-box setting stipulates that the exact semantics of the dynamical system and the controller are unknown, and that we are only able to observe the state of the controlled (aka, closed-loop) system at finitely many time points. We present a novel framework for predicting future states of the system based on the states observed in the past. The numbers of past states and of predicted future states are parameters provided by the user. Our method is based on a combination of Taylor’s expansion and the backward difference operator for numerical differentiation. We also derive an upper bound on the prediction error under the assumption that the system dynamics and the controller are smooth. The predicted states are then used to predict safety violations ahead in time. Our experiments demonstrate practical applicability of our method for complex black-box systems, showing that it is computationally lightweight and yet significantly more accurate than the state-of-the-art predictive safety monitoring techniques."}],"OA_type":"gold","alternative_title":["PMLR"],"project":[{"_id":"62781420-2b32-11ec-9570-8d9b63373d4d","grant_number":"101020093","call_identifier":"H2020","name":"Vigilant Algorithmic Monitoring of Software"}],"has_accepted_license":"1","arxiv":1,"publisher":"ML Research Press","publication":"7th Annual Learning for Dynamics & Control Conference","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","external_id":{"arxiv":["2412.16564"]},"language":[{"iso":"eng"}],"page":"804-816","citation":{"apa":"Henzinger, T. A., Kresse, F., Mallik, K., Yu, E., &#38; Zikelic, D. (2025). Predictive monitoring of black-box dynamical systems. In <i>7th Annual Learning for Dynamics &#38; Control Conference</i> (Vol. 283, pp. 804–816). Ann Arbor, MI, United States: ML Research Press.","ama":"Henzinger TA, Kresse F, Mallik K, Yu E, Zikelic D. Predictive monitoring of black-box dynamical systems. In: <i>7th Annual Learning for Dynamics &#38; Control Conference</i>. Vol 283. ML Research Press; 2025:804-816.","short":"T.A. Henzinger, F. Kresse, K. Mallik, E. Yu, D. Zikelic, in:, 7th Annual Learning for Dynamics &#38; Control Conference, ML Research Press, 2025, pp. 804–816.","mla":"Henzinger, Thomas A., et al. “Predictive Monitoring of Black-Box Dynamical Systems.” <i>7th Annual Learning for Dynamics &#38; Control Conference</i>, vol. 283, ML Research Press, 2025, pp. 804–16.","chicago":"Henzinger, Thomas A, Fabian Kresse, Kaushik Mallik, Emily Yu, and Dorde Zikelic. “Predictive Monitoring of Black-Box Dynamical Systems.” In <i>7th Annual Learning for Dynamics &#38; Control Conference</i>, 283:804–16. ML Research Press, 2025.","ista":"Henzinger TA, Kresse F, Mallik K, Yu E, Zikelic D. 2025. Predictive monitoring of black-box dynamical systems. 7th Annual Learning for Dynamics &#38; Control Conference. L4DC: Learning for Dynamics &#38; Control, PMLR, vol. 283, 804–816.","ieee":"T. A. Henzinger, F. Kresse, K. Mallik, E. Yu, and D. Zikelic, “Predictive monitoring of black-box dynamical systems,” in <i>7th Annual Learning for Dynamics &#38; Control Conference</i>, Ann Arbor, MI, United States, 2025, vol. 283, pp. 804–816."},"intvolume":"       283","corr_author":"1","scopus_import":"1","department":[{"_id":"ToHe"},{"_id":"ChLa"}],"title":"Predictive monitoring of black-box dynamical systems","_id":"20256","file_date_updated":"2025-09-03T10:32:12Z","date_updated":"2025-09-03T10:37:59Z","publication_identifier":{"eissn":["2640-3498"]},"date_published":"2025-06-01T00:00:00Z","conference":{"location":"Ann Arbor, MI, United States","name":"L4DC: Learning for Dynamics & Control","start_date":"2025-06-04","end_date":"2025-06-06"},"year":"2025","type":"conference","publication_status":"published","quality_controlled":"1","day":"01","date_created":"2025-08-31T22:01:32Z","file":[{"content_type":"application/pdf","file_name":"2025_L4DC_HenzingerT.pdf","date_created":"2025-09-03T10:32:12Z","relation":"main_file","file_id":"20283","creator":"dernst","date_updated":"2025-09-03T10:32:12Z","file_size":489639,"success":1,"access_level":"open_access","checksum":"d5236e561560635f5ae1d17de4903033"}],"month":"06"}