Chenran Xu
Information
Institute: IBPT
Room: R314, Bldg. 345, Campus Nord
Phone: 0721/608-23353
Email: chenran.xu@kit.edu
Doctoral Thesis
Preliminary Title: Accelerator Control with Machine Learning Methods at FLUTE
Research Interests & Topics:
- Reinforcement Learning Control
- Surrogate Modeling
- Accelerator Simulations: Ocelot, ASTRA, ...
- Virtual Diagnositcs, Digital Twin
- Bayesian Optimization, Gaussian Processes
- Spatial Light Modulator Laser Modulation
Supervisor: Prof. Dr. Anke-Susanne Müller
Scientific Advisor: Dr. Andrea Santamaria Garcia
Publications
| [1] | C. Xu et. al, Machine Learning Based Spatial Light Modulator Control for the Photoinjector Laser at FLUTE, in Proc. IPAC'21, Campinas, SP, Brazil, May 2021, pp. 3332--3335. [ DOI | KITopen ] |
| [2] | A. Eichler et. al, First Steps Toward an Autonomous Accelerator, a Common Project Between DESY and KIT, in Proc. IPAC'21, Campinas, SP, Brazil, May 2021, pp. 2182--2185. [ DOI | KITopen ] |
| [3] | C. Xu et. al, Optimization Studies of Simulated THz Radiation at FLUTE, in Proc. IPAC'22, Bangkok, Thailand, Jun. 2022, pp. 2292--2295. [ DOI ] |
| [4] | C. Xu et. al, Surrogate Modelling of the FLUTE Low-Energy Section, in Proc. IPAC'22, Bangkok, Thailand, Jun. 2022, pp. 1182-1185. [ DOI ] |
| [5] | M. Nabinger et. al, Transverse and Longitudinal Modulation of Photoinjection Pulses at FLUTE, in Proc. IPAC'22, Bangkok, Thailand, Jun. 2022, pp. 1174-1177. [ DOI ] |
| [6] | C. Xu et. al, Bayesian optimization of the beam injection process into a storage ring, Phys. Rev. Accel. Beams 26 (2023) no.3, 3. [ DOI | KITopen ] |
| [7] | C. Xu et. al, Bayesian Optimization for SASE Tuning at the European XFEL, in Proc. IPAC'23, Venice, Italy, May. 2023, pp. 4483-4486. [ DOI ] |
| [8] | C. Xu et. al, Beam Trajectory Control with Lattice-Agnostic Reinforcement Learning, in Proc. IPAC'23, Venice, Italy, May. 2023, pp. 4487-4490. [ DOI ] |
| [9] | C. Xu et. al, Integration of an Optimizer Framework into the Control System at KARA, in Proc. ICALEPCS'23, Cape Town, South Africa, Oct. 2023, pp. 570--574. [ DOI ] |
| [10] | A. Santamaria Garcia et. al, How Can Machine Learning Help Future Light Sources?, in Proc. FLS2023, Luzern, Switzerland, Sep. 2023. [ Pre-proceeding ] |
Conferences and Workshops
| Title | Conferenece | Location | Date | Contribution |
|---|---|---|---|---|
| Bayesian optimization of injection efficiency at KARA using Gaussian processes [KITopen] | DPG Spring Meeting | Dortmund (virtual) | 03.2021 | Talk |
| Optimization Studies of Simulated THz Radiation at FLUTE [KITopen] | DPG Spring Meeting | Mainz (virtual) | 03.2022 | Talk |
| Optimization Studies of Simulated THz Radiation at FLUTE [KITopen] | 10th MT-ARD ST3 Annual Meeting | Berlin | 09.2022 | Speed Talk |
| Surrogate Modelling of the FLUTE Low-Energy Section [KITopen] | 8. Annual MT Meeting | Hamburg | 09.2022 | Poster |
| THz Radiation Optimization at Linac using Machine Learning Methods [KITopen] | 3rd ICFA Beam Dynamics ML-Workshop | Chicago | 11.2022 | Poster |
| Beam Trajectory Control with Lattice-Agnostic Reinforcement Learning [KITopen] | DPG Spring Meeting | Dresden | 03.2023 | Talk |
| Reinforcement Learning Applications at Particle Accelerators [KITopen] | 11th MT-ARD ST3 Annual Meeting | Dresden | 07.2023 | Talk |
| Beam Trajectory Control with Lattice-Agnostic Reinforcement Learning | 9. Annual MT Meeting | Karlsruhe | 10.2023 | Poster |