Simon Hirländer

Position: Postdoc-Position - Leiter des Teams "Smart Analytics & Reinforcement Learning"

E-Mail: simon.hirlaender@plus.ac.at

Website: Persönliche Webseite

Datum Beginn: 01.09.2020

Veröffentlichungen

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 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 Delayed-Consequence Accelerator Control. (2026) https://doi.org/https://doi.org/10.18429/JACoW-IPAC2026-WEP6097
S. Hirländer, B. Halilovic, P. Madysa, S. Appel: Robust real-time optimization 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 Meet 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 Optimization 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: Ph.D. 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 optimitization 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 Optimization: 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