@article{nokey,

title = {Constrained collaborative optimization of charged particle tracking with multi-agent reinforcement learning},

author = {T. Kortus, R. Keidel, N.R. Gauger, J. Kieseler, Bergen pCT Collaboration},

url = {http://iopscience.iop.org/article/10.1088/2632-2153/ae352b},

doi = {10.1088/2632-2153/ae352b},

year  = {2026},

date = {2026-01-07},

journal = {Machine Learning: Science and Technology},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Stochastic Dynamical Low-Rank Approximation in the Context of Machine Learning},

author = {A. Hnatiuk, J. Kusch, L. Kusch, N.R. Gauger, A. Walther},

url = {https://doi.org/10.1007/s10957-025-02892-1},

year  = {2026},

date = {2026-01-01},

urldate = {2026-01-01},

journal = {Journal of Optimization Theory and Applications},

volume = {208},

number = {63},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Progress in end-to-end optimization of fundamental physics experimental apparata with differentiable programming},

author = {M. Aehle, L. Arsini, R. Belén Barreiro, A. Belias, A. Boldyrev, F. Bury, S. Cebrian, A. Demin, J. Dickinson, J. Donini, T. Dorigo, M. Doro, N. R. Gauger, et al.},

url = {https://doi.org/10.1016/j.revip.2025.100120},

year  = {2025},

date = {2025-12-01},

urldate = {2025-12-01},

journal = {Reviews in Physics},

volume = {13},

pages = {100120},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Adjoint-based multi-point optimization framework for mixing processes applied to a venturi mixer},

author = {L. Kusch, N. Beishuizen, C. Morales Ubal,N.R. Gauger},

url = {https://doi.org/10.1016/j.applthermaleng.2025.128609},

year  = {2025},

date = {2025-12-01},

urldate = {2026-12-01},

journal = {Applied Thermal Engineering},

volume = {280},

number = {128609},

issue = {2},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Toward the end-to-end optimization of the SWGO array layout},

author = {T. Dorigo, M. Aehle, C. Arcaro, M. Awais, F. Bergamaschi, J. Donini, M. Doro, N. R. Gauger, R. Izbicki, J. Kieseler, A. Lee, L. Masserano, F. Nardi, R. Rajesh, L. Recabarren Vergara, A. Shen},

url = {https://doi.org/10.1016/j.nuclphysb.2025.116934},

year  = {2025},

date = {2025-08-01},

urldate = {2025-08-01},

journal = {Nuclear Physics B},

volume = {1017},

pages = {116934},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Reconstruction of proton relative stopping power with a granular calorimeter detector model},

author = {M. Aehle, J. Alme, G.G. Barnaföldi, G. Bíró, T. Bodova, V. Borshchov, A. van den Brink, M. Chaar, B. Dudás, V. Eikeland, G. Feofilov, C. Garth, N.R. Gauger, O. Grøttvik, H. Helstrup, S. Igolkin, Z. Jólesz, R. Keidel, C. Kobdaj, T Kortus, et al.},

url = {https://www.worldscientific.com/doi/10.1142/S0217751X25420084},

year  = {2025},

date = {2025-07-31},

urldate = {2025-07-31},

journal = {International Journal of Modern Physics A},

volume = {40},

number = {21},

pages = {2542008},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Exploring End-to-end Differentiable Neural Charged Particle Tracking – A Loss Landscape Perspective},

author = {T. Kortus, R. Keidel, N.R. Gauger, Bergen pCT Collaboration },

editor = {Atsushi Nitanda},

url = {https://openreview.net/forum?id=1Pi2GwduEz},

issn = {2835-8856},

year  = {2025},

date = {2025-07-30},

urldate = {2025-07-30},

journal = {Transactions on Machine Learning Research},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ABSG2025,

title = {Forward-Mode Automatic Differentiation of Compiled Programs},

author = {M. Aehle, J. Blühdorn, M. Sagebaum, N. R. Gauger},

doi = {10.1145/3716309},

year  = {2025},

date = {2025-05-15},

urldate = {2025-05-15},

journal = {ACM Transactions on Mathematical Software},

volume = {51},

number = {7},

issue = {2},

pages = {1-25},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {On the utility function of experiments in fundamental science},

author = {T. Dorigo, M. Doro, M. Aehle, M. Awais, N. R. Gauger, R. Izbicki, J. Kieseler, A. B. Lee, L. Masserano, F. Nardi, A. Shen, L. Recabarren Vergara},

url = {https://doi.org/10.1016/j.physo.2025.100270},

year  = {2025},

date = {2025-04-25},

urldate = {2025-04-25},

journal = {Physics Open},

pages = {100270},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {End-to-End Detector Optimization with Diffusion Models: A Case Study in Sampling Calorimeters},

author = {K. Schmidt, K. N Kota, J. Kieseler, A. De Vita, M. Klute, Abhishek, M. Aehle, M. Awais, A. Breccia, R. Carroccio, L. Chen, T. Dorigo, N. R. Gauger, et al.},

url = {https://www.mdpi.com/2571-712X/8/2/47},

year  = {2025},

date = {2025-04-23},

urldate = {2025-04-23},

journal = {Particles},

volume = {8},

issue = {2},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Modeling charge collection in silicon pixel detectors for proton therapy applications},

author = {A. Schilling, M. Aehle, J. Alme, G. G. Barnaföldi, G. Bíró, T. Bodova, V. Borshchov, A. van den Brink, V. Eikeland, G. Feofilov, C. Garth, N. R. Gauger, O. Grøttvik, H. Helstrup, S. Igolkin, J. G. Johansen, R. Keidel, C. Kobdaj, T. Kortus, et al.},

url = {https://iopscience.iop.org/article/10.1088/2057-1976/adbf9c/meta},

year  = {2025},

date = {2025-03-24},

urldate = {2025-03-24},

journal = {Biomedical Physics & Engineering Express},

volume = {11},

number = {3},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Neuromorphic Readout for Hadron Calorimeters},

author = {E. Lupi, Abhishek, M. Aehle, M. Awais, A. Breccia, R. Carroccio, L. Chen, A. Das, A. De Vita, T. Dorigo, N. R. Gauger, R. Keidel, et al.},

url = {https://www.mdpi.com/2571-712X/8/2/52

http://arxiv.org/abs/2502.12693},

year  = {2025},

date = {2025-02-18},

urldate = {2025-02-18},

journal = {Particles},

volume = {8},

issue = {2},

howpublished = {Particles 8(2), 58},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Hadron Identification Prospects With Granular Calorimeters},

author = {A. De Vita, Abhishek, M. Aehle, M. Awais, A. Breccia, R. Carroccio, L. Chen, T. Dorigo, N. R. Gauger, R. Keidel, et al.},

url = {https://www.mdpi.com/2571-712X/8/2/58

http://arxiv.org/abs/2502.10817},

year  = {2025},

date = {2025-02-15},

urldate = {2025-02-15},

journal = {Particles},

volume = {8},

issue = {2},

howpublished = {Particles 8(2), 58},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{USERN2025,

title = {Artificial Intelligence in Science and Society: the Vision of USERN},

author = {T. Dorigo, G.D. Brown, C. Casonato, A. Cerdà, J. Ciarrochi, M. Da Lio, N. D’Souza, N.R. Gauger, et al. },

url = {https://ieeexplore.ieee.org/abstract/document/10839366},

doi = {10.1109/ACCESS.2025.3529357},

year  = {2025},

date = {2025-01-13},

journal = {IEEE Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Optimization using pathwise algorithmic derivatives of electromagnetic shower simulations},

author = {M. Aehle, M. Novák, V. Vassilev, N. R. Gauger, L. Heinrich, M. Kagan, D. Lange},

doi = {10.1016/j.cpc.2024.109491},

year  = {2025},

date = {2025-01-08},

urldate = {2025-04-01},

journal = {Computer Physics Communications},

volume = {309},

pages = {109491},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{BGAG2025,

title = {Hybrid parallel discrete adjoints in SU2},

author = {J. Blühdorn, P. Gomes, M. Aehle, N. R. Gauger},

doi = {10.1016/j.compfluid.2024.106528},

year  = {2025},

date = {2025-01-02},

urldate = {2025-01-02},

journal = {Computers & Fluids},

volume = {289},

number = {106528},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Development of the trigger-controlling system for the proton computed tomography prototype},

author = {N. Ritjoho, P. Phumara, Y. Buanill, P. Boontueng, A. Songmoolnak, N. Laojamnongwong, L. Charoonratana, T. Sanghangthum, D. Röhrich, A. Schilling, M. Aehle, J. Alme, G.G. Barnaföldi, G. Bíro, T. Bodova, V. Borshchov, A. van den Brink, V. Eikeland, G. Feofilov, C. Garth, N.R. Gauger, et al.},

url = {https://www.sciencedirect.com/science/article/abs/pii/S0168900224010519},

year  = {2025},

date = {2025-01-01},

urldate = {2025-03-01},

journal = {Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment},

volume = {1072},

pages = {170125},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Parametric Aerodynamic Shape Optimization with Latent Diffusion},

author = {L. Chen, J. Rottmayer, T. Kortus, E. Özkaya, N.R. Gauger, Y. Ye},

year  = {2024},

date = {2024-11-13},

urldate = {2024-11-13},

journal = {Jahresbericht der Deutschen Strömungsmechanischen Arbeitsgemeinschaft STAB},

pages = {86-87},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Generalized Derivative Enhanced Surrogate Modeling Framework for Aerodynamic Design Optimization},

author = {E. Öykaza, N.R. Gauger},

year  = {2024},

date = {2024-11-13},

journal = {Jahresbericht der Deutschen Strömungsmechanischen Arbeitsgemeinschaft STAB},

pages = {94-95},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Sobolev Training for BNN Surrogates in Efficient Global Optimization},

author = {J. Rottmayer, L. Chen, E. Özkaya, N.R. Gauger},

year  = {2024},

date = {2024-11-13},

urldate = {2024-11-13},

journal = {Jahresbericht der Deutschen Strömungsmechanischen Arbeitsgemeinschaft STAB},

pages = {96-97},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{aerospace11080607,

title = {Nonlinear Surrogate Model Design for Aerodynamic Dataset Generation Based on Artificial Neural Networks},

author = {G. Suarez, E. Özkaya, N.R. Gauger, H. Steiner, M. Schäfer, D. Naumann},

doi = {10.3390/aerospace11080607},

issn = {2226-4310},

year  = {2024},

date = {2024-07-24},

urldate = {2024-07-24},

journal = {Aerospace},

volume = {11},

issue = {8},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Performance of the electromagnetic and hadronic prototype segments of the ALICE Forward Calorimeter},

author = {M. Aehle, J. Alme, C. Arata, I. Arsene, I. Bearden, T. Bodova, V. Borshchov, O. Bourrion, M. Bregant, A. van den Brink, V. Buchakchiev, A. Buhl, T. Chujo, L. Dufke, V. Eikeland, M. Fasel, N. Gauger, A. Gautam, A. Ghimouz, Y. Goto, R. Guernane, T. Hachiya, H. Hassan, L. He, H. Helstrup, L. Huhta, M. Inaba, T. Isidori, F. Jonas, T.Kawaguchi, R. Keidel, M. Kim, V. Kozhuharov, T. Kumaoka, L. Kusch, et al.},

url = {https://iopscience.iop.org/article/10.1088/1748-0221/19/07/P07006/meta},

doi = {10.1088/1748-0221/19/07/P07006},

year  = {2024},

date = {2024-07-05},

urldate = {2024-07-05},

journal = {Journal of Instrumentation},

volume = {19},

number = {P07006},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokeyr,

title = {Data-driven aerodynamic shape design with distributionally robust optimization approaches},

author = {L. Chen, J. Rottmayer, L. Kusch, N. R.Gauger, Y. Ye, },

url = {https://doi.org/10.1016/j.cma.2024.117131},

issn = {0045-7825},

year  = {2024},

date = {2024-06-15},

urldate = {2024-06-15},

journal = {Computer Methods in Applied Mechanics and Engineering},

volume = {429},

pages = {117131},

abstract = {We formulate and solve data-driven aerodynamic shape design problems with distributionally robust optimization (DRO) approaches. DRO aims to minimize the worst-case expected performance in a set of distributions that is informed by observed data with uncertainties. Building on the findings of the work Gotoh, et al. (2018), we study the connections between a class of DRO and robust design optimization, which is classically based on the mean–variance (standard deviation) optimization formulation pioneered by Taguchi. Our results provide a new perspective to the understanding and formulation of robust design optimization problems. It enables data-driven and statistically principled approaches to quantify the trade-offs between robustness and performance, in contrast to the classical robust design formulation that captures uncertainty only qualitatively. Our preliminary computational experiments on aerodynamic shape optimization in transonic turbulent flow show promising design results.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}