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  • (-) PSI Groups = 4805 Accelerator Modelling and Advanced Simulations
  • (-) Keywords ≠ Particle-In-Cell
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Multiobjective optimization of the dynamic aperture using surrogate models based on artificial neural networks
Kranjčević, M., Riemann, B., Adelmann, A., & Streun, A. (2021). Multiobjective optimization of the dynamic aperture using surrogate models based on artificial neural networks. Physical Review Accelerators and Beams, 24(1), 014601 (15 pp.). https://doi.org/10.1103/PhysRevAccelBeams.24.014601
A novel approach for classification and forecasting of time series in particle accelerators
Li, S., Zacharias, M., Snuverink, J., Coello de Portugal, J., Perez-Cruz, F., Reggiani, D., & Adelmann, A. (2021). A novel approach for classification and forecasting of time series in particle accelerators. Information, 12(3), 121 (21 pp.). https://doi.org/10.3390/info12030121
Sparse grid-based adaptive noise reduction strategy for particle-in-cell schemes
Muralikrishnan, S., Cerfon, A. J., Frey, M., Ricketson, L. F., & Adelmann, A. (2021). Sparse grid-based adaptive noise reduction strategy for particle-in-cell schemes. Journal of Computational Physics: X, 11, 100094 (31 pp.). https://doi.org/10.1016/j.jcpx.2021.100094
OPAL-MITHRA: self-consistent software for start-to-end simulation of undulator-based facilities
Albà, A., Adelmann, A., & Fallahi, A. (2020). OPAL-MITHRA: self-consistent software for start-to-end simulation of undulator-based facilities. In 2020 33rd international vacuum nanoelectronics conference (IVNC). 33rd international vacuum nanoelectronics conference (IVNC) July 6-7, 2020 (p. 9203404 (2 pp.). https://doi.org/10.1109/IVNC49440.2020.9203404
Beam stripping interactions implemented in cyclotrons with opal simulation code
Calvo, P., Oliver, C., Adelmann, A., Frey, M., Gsell, A., & Snuverink, J. (2020). Beam stripping interactions implemented in cyclotrons with opal simulation code. In L. Conradie, J. Garrett De Villiers, & V. R. W. Schaa (Eds.), International conference on cyclotrons and their applications: Vol. 22. CYC2019. 22nd international conference on cyclotrons and their applications (pp. 109-112). https://doi.org/10.18429/JACoW-Cyclotrons2019-MOP034
Machine learning for orders of magnitude speedup in multiobjective optimization of particle accelerator systems
Edelen, A., Neveu, N., Frey, M., Huber, Y., Mayes, C., & Adelmann, A. (2020). Machine learning for orders of magnitude speedup in multiobjective optimization of particle accelerator systems. Physical Review Accelerators and Beams, 23(4), 044601 (23 pp.). https://doi.org/10.1103/PhysRevAccelBeams.23.044601
Global sensitivity analysis on numerical solver parameters of particle-in-cell models in particle accelerator systems
Frey, M., & Adelmann, A. (2020). Global sensitivity analysis on numerical solver parameters of particle-in-cell models in particle accelerator systems. Computer Physics Communications, 258, 107577 (17 pp.). https://doi.org/10.1016/j.cpc.2020.107577
Uncertainty quantification analysis and optimization for proton therapy beam lines
Rizzoglio, V., Adelmann, A., Gerbershagen, A., Meer, D., Nesteruk, K. P., & Schippers, J. M. (2020). Uncertainty quantification analysis and optimization for proton therapy beam lines. Physica Medica, 75, 11-18. https://doi.org/10.1016/j.ejmp.2020.05.013
On nonintrusive uncertainty quantification and surrogate model construction in particle accelerator modeling
Adelmann, A. (2019). On nonintrusive uncertainty quantification and surrogate model construction in particle accelerator modeling. SIAM-ASA Journal on Uncertainty Quantification, 7(2), 383-416. https://doi.org/10.1137/16M1061928
Matching of turn pattern measurements for cyclotrons using multiobjective optimization
Frey, M., Snuverink, J., Baumgarten, C., & Adelmann, A. (2019). Matching of turn pattern measurements for cyclotrons using multiobjective optimization. Physical Review Accelerators and Beams, 22(6), 064602 (13 pp.). https://doi.org/10.1103/PhysRevAccelBeams.22.064602
Bayesian optimisation for fast and safe parameter tuning of SwissFEL
Kirschner, J., Nonnenmacher, M., Mutný, M., Krause, A., Hiller, N., Ischebeck, R., & Adelmann, A. (2019). Bayesian optimisation for fast and safe parameter tuning of SwissFEL. In W. Decking, H. Sinn, G. Geloni, S. Schreiber, M. Marx, & V. R. W. Schaa (Eds.), Free electron laser conference: Vol. 39. 39th international free-electron laser conference. FEL2019. Proceedings (pp. 707-710). https://doi.org/10.18429/JACoW-FEL2019-THP061
Constrained multiobjective shape optimization of superconducting rf cavities considering robustness against geometric perturbations
Kranjčević, M., Gorgi Zadeh, S., Adelmann, A., Arbenz, P., & van Rienen, U. (2019). Constrained multiobjective shape optimization of superconducting rf cavities considering robustness against geometric perturbations. Physical Review Accelerators and Beams, 22(12), 122001 (14 pp.). https://doi.org/10.1103/PhysRevAccelBeams.22.122001
Multi-objective shape optimization of radio frequency cavities using an evolutionary algorithm
Kranjčević, M., Adelmann, A., Arbenz, P., Citterio, A., & Stingelin, L. (2019). Multi-objective shape optimization of radio frequency cavities using an evolutionary algorithm. Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 920, 106-114. https://doi.org/10.1016/j.nima.2018.12.066
Large energy acceptance gantry for proton therapy utilizing superconducting technology
Nesteruk, K. P., Calzolaio, C., Meer, D., Rizzoglio, V., Seidel, M., & Schippers, J. M. (2019). Large energy acceptance gantry for proton therapy utilizing superconducting technology. Physics in Medicine and Biology, 64(17), 175007 (13 pp.). https://doi.org/10.1088/1361-6560/ab2f5f
Parallel general purpose multiobjective optimization framework with application to electron beam dynamics
Neveu, N., Spentzouris, L., Adelmann, A., Ineichen, Y., Kolano, A., Metzger-Kraus, C., … Arbenz, P. (2019). Parallel general purpose multiobjective optimization framework with application to electron beam dynamics. Physical Review Accelerators and Beams, 22(5), 054602 (11 pp.). https://doi.org/10.1103/PhysRevAccelBeams.22.054602
Challenges in simulating beam dynamics of dielectric laser acceleration
Niedermayer, U., Adelmann, A., Bettoni, S., Calvi, M., Dehler, M., Ferrari, E., … Simakov, E. (2019). Challenges in simulating beam dynamics of dielectric laser acceleration. International Journal of Modern Physics A, 34(36), 1942031 (15 pp.). https://doi.org/10.1142/S0217751X19420314
Calculation of longitudinal collective instabilities with <em>mbtrack-cuda</em>
Xu, H., Locans, U., Adelmann, A., & Stingelin, L. (2019). Calculation of longitudinal collective instabilities with mbtrack-cuda. Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 922, 345-351. https://doi.org/10.1016/j.nima.2019.01.041
Fixed Field Accelerators and space charge modeling
Adelmann, A., Rogers, C., & Sheehy, S. L. (2018). Fixed Field Accelerators and space charge modeling. In V. R. W. Schaa (Ed.), ICFA advanced beam dynamics workshop: Vol. 61. Proceedings of the 61st ICFA advanced beam dynamics workshop on high-intensity and high-brightness hadron beams (pp. 158-162). https://doi.org/10.18429/JACoW-HB2018-TUP1WA02
Real-time tomography of gas-jets with a Wollaston Interferometer
Adelmann, A., Hermann, B., Ischebeck, R., Kaluza, M. C., Locans, U., Sauerwein, N., & Tarkeshian, R. (2018). Real-time tomography of gas-jets with a Wollaston Interferometer. Applied Sciences, 8(3), 443 (21 pp.). https://doi.org/10.3390/app8030443
Intensity limits of the PSI Injector II cyclotron
Kolano, A., Adelmann, A., Barlow, R., & Baumgarten, C. (2018). Intensity limits of the PSI Injector II cyclotron. Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 885, 54-59. https://doi.org/10.1016/j.nima.2017.12.045