Simon Hirländer
Dr Simon Hirländer heads the "Smart Analytics & Reinforcement Learning" team. He gained several years of experience in international research at CERN, where he also obtained his PhD. In recent years, he has successfully focused on optimising the performance of the CERN accelerator complex using machine learning, in particular the application of reinforcement learning.
Simon Hirländer
Position: Postdoc position - Head of the "Smart Analytics & Reinforcement Learning" team
E-Mail: simon.hirlaender@plus.ac.at
Webseite: Persönliche Webseite
Datum Beginn: 01.09.2020
Publikationen
- S. Appel, H. Alsmeier, M. Bajzek, O. Boine-Frankenheim, L. Dingeldein, R. Findeisen, B. Halilovic, S. Hirländer: Automating Accelerator Tuning at GSI/FAIR. (2026) https://doi.org/https://doi.org/10.1007/s41781-026-00175-6
- J. Langschwert, G. Schäfer, J. Rehrl, S. Huber, S. Hirländer: Reinforcement Learning for Optimal Experiment Design in the Parameter Identification of Mechatronic Systems. (2026) https://doi.org/https://doi.org/10.1007/978-3-032-34202-7_11
- G. Schäfer, J. Rehrl, S. Huber, S. Hirländer: Anticipatory Reinforcement Learning for Trajectory Tracking. (2026) https://doi.org/https://doi.org/10.1007/978-3-032-34202-7_9
- A. Kokoefer, A. G. Deribe, G. Zimmermann, S. Hirländer, B. Wernly, et al.: Structural imbalances in open-access critical care research: a bibliometric analysis of abstracts from ICU database studies, 2018–2025. (2026) https://doi.org/https://doi.org/10.1186/s13054-026-06274-5
- S. Hirländer, O. Mironova, S. Trausner, L. Grech, L. Fischl, A. Santamaria Garcia: Koopman-Stabilised World Models for Offline Reinforcement Learning in Accelerator Control. (2026) https://doi.org/ https://doi.org/10.18429/JACoW-IPAC2026-WEP6098
- S. Hirländer, K. Björkbom, S. Trausner, O. Mironova, L. Fischl, P. Auer, R. Ortner, V. Kain: Reinforcement Learning Beyond Greedy Optimisation for Accelerator Control with Delayed Consequences. (2026) https://doi.org/https://doi.org/10.18429/JACoW-IPAC2026-WEP6097
- S. Hirländer, B. Halilovic, P. Madysa, S. Appel: Robust real-time optimisation of SIS18 injection using Gaussian Process MPC. (2026) https://doi.org/https://doi.org/10.18429/JACoW-IPAC2026-THP4097
- S. Hirländer, O. Mironova, S. Trausner, L. Fischl, T. Gallien, L. Grech: Causal GP-MPC: Where Structure, Safety and Online Learning Come Together for Robust Accelerator Control. (2026) https://doi.org/https://doi.org/10.18429/JACoW-IPAC2026-WEP6096
- L. Grech, M. G. Krauss, M. Consiglio, T. J. G. Apollaro, C. P. Koch, S. Hirländer, G. Valentino: Achieving fast and robust perfect entangling gates via reinforcement learning. (2025) https://doi.org/https://doi.org/10.1088/2058-9565/ae2c16
- A. Santamaria Garcia, C. Xu, J. Kaiser, B. R. Mateos, C. Hespe, A. Eichler, S. Hirländer: Efficient data-driven model predictive control for online accelerator tuning. (2025) https://doi.org/https://doi.org/10.18429/JACoW-IPAC2025-THPM116
- G. Schäfer, R. Seliger, J. Rehrl, S. Huber, S. Hirländer: Multi-objective Reinforcement Learning for Energy-Efficient Industrial Control. (2025) https://doi.org/https://doi.org/10.1007/978-3-032-02003-1_6
- V. Kain, B. Rodriguez Mateos, N. Bruchon, D. Küchler, S. Hirländer: Continuous data-driven control of the GTS-LHC ion source at CERN. (2025) https://doi.org/https://doi.org/10.18429/JACoW-ECRIS2024-MOP11
- G. Schäfer, J. Rehrl, S. Huber, S. Hirländer: Comparison of Model Predictive Control and Proximal Policy Optimisation for a 1-DOF Helicopter System. (2024) https://doi.org/https://doi.org/10.1109/INDIN58382.2024.10774357
- G. Schäfer, S. Huber, S. Hirländer, et al: Python-Based Reinforcement Learning on Simulink Models. (2024) https://doi.org/ https://doi.org/10.1007/978-3-031-65993-5_55
- S. Hirländer, S. Pochaba, C. Xu, et al: Deep Meta Reinforcement Learning for Rapid Adaptation In Linear Markov Decision Processes: Applications to CERN's AWAKE Project. (2024) https://doi.org/ https://doi.org/10.1007/978-3-031-65993-5_21
- S. Pochaba, R. Kwitt, S. Hirländer, et al: Multi-agent Reinforcement Learning and Its Application to Wireless Network Communication. (2024) https://doi.org/ https://doi.org/10.1007/978-3-031-65993-5_45
- S. Pochaba, P. Dorfinger, M. Herlich, R. Kwitt, S. Hirländer: PhD Forum: Multi-agent reinforcement learning in wireless network communication. (2024) https://doi.org/https://doi.org/ 10.1109/WoWMoM60985.2024.00058
- A. Santamaría, C. Xu, L. Scomparin, S. Hirländer, S. Pochaba, A. Eichler, J. Kaiser, M. Schenk : The Reinforcement Learning for Autonomous Accelerators Collaboration. (2024) https://doi.org/https://doi.org/10.18429/JACoW-IPAC2024-TUPS62
- S. Hirländer, S. Appel, N. Madysa: Data-Driven model predictive control for automated optimitisation of injection into the SIS18 synchrotron. (2024) https://doi.org/ https://doi.org/10.18429/JACoW-IPAC2024-TUPS59
- S. Hirländer, L. Lamminger, S. Pochaba, J. Kaiser, C. Xu, A. Santamaría, L. Scomparin, V. Kain : Towards few-shot reinforcement learning in particle accelerator control. (2024) https://doi.org/ https://doi.org/10.18429/JACoW-IPAC2024-TUPS59
- R. Kozlica, G. Schäfer, S. Hirländer, S. Wegenkittl: A Modular Test Bed for Reinforcement Learning Incorporation into Industrial Applications. (2024) https://doi.org/ https://doi.org/10.1007/978-3-031-42171-6_15
- A. Oeftiger, S. Garcia, J. Lagrange, S. Hirländer : Active Deep Learning for Nonlinear Optics Design of a Vertical FFA Accelerato. (2023) https://doi.org/https://doi.org/10.18429/jacow-ipac2023-wepa026
- S. Hirländer, L. Lamminger, G. Zevi-Della-Pora, V. Kain : Ultra fast reinforcement learning in accelerator control demonstrated on CERN AWAKE. (2023) https://doi.org/https://doi.org/10.18429/jacow-ipac2023-thpl038
- R. Kozlica, S. Wegenkittl, S. Hirländer: Deep Q-Learning versus Proximal Policy Optimisation: Performance Comparison in a Material Sorting Task. (2023) https://doi.org/https://doi.org/10.1109/isie51358.2023.10228056
- F.M. Velotti, B. Goddard, V. Kain, R. Ramjiawan, G. Zevi Della Porta, S. Hirländer: Towards automatic setup of 18 MeV electron beamline using machine learning. (2023) https://doi.org/ https://doi.org/10.1088/2632-2153/acce21
- F.M. Velotti, B. Goddard, V. Kain, R. Ramjiawan, G.Z.D. Porta, S. Hirländer: Automatic setup of 18 MeV electron beamline using machine learning. (2022) https://doi.org/ https://doi.org/10.48550/arXiv.2209.03183
- L. Grech, G. Valentino, D. Alves, S. Hirländer: Application of reinforcement learning in the LHC tune feedback. (2022) https://doi.org/ https://doi.org/10.3389/fphy.2022.929064
- V. Kain, N. Bruchon, S. Hirländer, N. Madysa, I. Vojskovic, P.K. Skowronski, G. Valentino: Test of Machine Learning at the Cern LINAC4. (2021) https://doi.org/https://doi.org/10.18429/JACoW-HB2021-TUEC4
- F. Kröger, G. Weber, S. Hirländer, R. Alemany-Fernández, M. W. Krasny, T. Stohlker, I. Tolstikhina, V. Shevelko: Charge-state distributions of highly charged lead ions at relativistic collision energies. (2021) https://doi.org/ https://doi.org/10.1002/andp.202100245
- N. Bruchon, G. Fenu, G. Gaio, S. Hirländer, M. Lonza, F.A. Pellegrino, E. Salvato: An Online Iterative Linear Quadratic Approach for a Satisfactory Working Point Attainment at FERMI. (2021) https://doi.org/https://doi.org/10.3390/info12070262
- V. Kain, S. Hirländer, B. Goddard, F.M.Velotti, G. Zevi Della Porta, N. Bruchon, G. Valentino: Sample-efficient reinforcement learning for CERN accelerator control. (2020) https://doi.org/ https://doi.org/10.1103/PhysRevAccelBeams.23.124801
- S. Hirländer, N. Bruchon: Model-free and Bayesian Ensembling Model-based Deep Reinforcement Learning for Particle Accelerator Control Demonstrated on the FERMI FEL. (2020) https://doi.org/ https://doi.org/10.48550/arXiv.2006.10330