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Fast uncertainty quantification of spent nuclear fuel with neural networks
Albà, A., Adelmann, A., Münster, L., Rochman, D., & Boiger, R. (2024). Fast uncertainty quantification of spent nuclear fuel with neural networks. Annals of Nuclear Energy, 196, 110204 (8 pp.). https://doi.org/10.1016/j.anucene.2023.110204
Computational models for high-power cyclotrons and FFAs
Adelmann, A., & Rogers, C.  T. (2023). Computational models for high-power cyclotrons and FFAs. Journal of Instrumentation, 18(3), T03006 (8 pp.). https://doi.org/10.1088/1748-0221/18/03/T03006
Saliency-enhanced content-based image retrieval for diagnosis support in dermatology consultation: reader study
Gassner, M., Barranco Garcia, J., Tanadini-Lang, S., Bertoldo, F., Fröhlich, F., Guckenberger, M., … Braun, R. P. (2023). Saliency-enhanced content-based image retrieval for diagnosis support in dermatology consultation: reader study. JMIR Dermatology, 6(1), e42129 (10 pp.). https://doi.org/10.2196/42129
Time series forecasting methods and their applications to particle accelerators
Li, S., & Adelmann, A. (2023). Time series forecasting methods and their applications to particle accelerators. Physical Review Accelerators and Beams, 26(2), 024801 (16 pp.). https://doi.org/10.1103/PhysRevAccelBeams.26.024801
High-power fixed-field accelerators
Winklehner, D., Adelmann, A., Alonso, J. R., Calabretta, L., Okuno, H., Planche, T., & Haj Tahar, M. (2023). High-power fixed-field accelerators. Journal of Instrumentation, 18(5), T05008 (59 pp.). https://doi.org/10.1088/1748-0221/18/05/T05008
Benchmarking collective effects of electron interactions in a wiggler with OPAL-FEL
Albà, A., Seok, J., Adelmann, A., Doran, S., Ha, G., Lee, S., … Zholents, A. (2022). Benchmarking collective effects of electron interactions in a wiggler with OPAL-FEL. Computer Physics Communications, 280, 108475 (9 pp.). https://doi.org/10.1016/j.cpc.2022.108475
IsoDAR@Yemilab: a report on the technology, capabilities, and deployment
Alonso, J. R., Conrad, J. M., Winklehner, D., Spitz, J., Bartoszek, L., Adelmann, A., … Waites, L. H. (2022). IsoDAR@Yemilab: a report on the technology, capabilities, and deployment. Journal of Instrumentation, 17(9), P09042 (31 pp.). https://doi.org/10.1088/1748-0221/17/09/P09042
Retrieval of aerosol properties from in situ, multi-angle light scattering measurements using invertible neural networks
Boiger, R., Modini, R. L., Moallemi, A., Degen, D., Adelmann, A., & Gysel-Beer, M. (2022). Retrieval of aerosol properties from in situ, multi-angle light scattering measurements using invertible neural networks. Journal of Aerosol Science, 163, 105977 (20 pp.). https://doi.org/10.1016/j.jaerosci.2022.105977
Search for the muon electric dipole moment using frozen-spin technique at PSI
Khaw, K. S., Adelmann, A., Backhaus, M., Berger, N., Daum, M., Giovannozzi, M., … Schmidt-Wellenburg, P. (2022). Search for the muon electric dipole moment using frozen-spin technique at PSI. In W. M. Bonviento (Ed.), Proceedings of science: Vol. 402. The 22nd international workshop on neutrinos from accelerators (NuFact2021) (p. 136 (5 pp.). https://doi.org/10.22323/1.402.0136
Input beam matching and beam dynamics design optimizations of the IsoDAR RFQ using statistical and machine learning techniques
Koser, D., Waites, L., Winklehner, D., Frey, M., Adelmann, A., & Conrad, J. (2022). Input beam matching and beam dynamics design optimizations of the IsoDAR RFQ using statistical and machine learning techniques. Frontiers in Physics, 10, 875889 (10 pp.). https://doi.org/10.3389/fphy.2022.875889
Order-of-magnitude beam current improvement in compact cyclotrons
Winklehner, D., Conrad, J. M., Schoen, D., Yampolskaya, M., Adelmann, A., Mayani, S., & Muralikrishnan, S. (2022). Order-of-magnitude beam current improvement in compact cyclotrons. New Journal of Physics, 24(2), 023038 (22 pp.). https://doi.org/10.1088/1367-2630/ac5001
Fast, efficient and flexible particle accelerator optimisation using densely connected and invertible neural networks
Bellotti, R., Boiger, R., & Adelmann, A. (2021). Fast, efficient and flexible particle accelerator optimisation using densely connected and invertible neural networks. Information, 12(9), 351 (21 pp.). https://doi.org/10.3390/INFO12090351
Beam stripping interactions in compact cyclotrons
Calvo, P., Podadera, I., Gavela, D., Oliver, C., Adelmann, A., Snuverink, J., & Gsell, A. (2021). Beam stripping interactions in compact cyclotrons. Physical Review Accelerators and Beams, 24(9), 090101 (11 pp.). https://doi.org/10.1103/PhysRevAccelBeams.24.090101
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
 

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