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Currently, over half of the computing power at CERN GRID is used to run High Energy Physics simulations.
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Arnaldi, R., Chiavassa, E., Cicalò, C., Cortese, P., De Falco, A., Dellacasa, G., De Marco, N., Ferretti, A., Gallio, M., Gemme, R., Masoni, A., Mereu, P., Musso, A., Oppedisano, C., Piccotti, A., Poggio, F., Puddu, G., Scomparin, E., Serci, S., Siddi, E., Travaglia, G., Usai, G., Vercellin, E.: The neutron zero degree calorimeter for the alice experiment. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
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Kingma, D.P., Welling, M.: Auto-encoding variational bayes. CoRR
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Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in neural information processing systems. pp. 2672–2680 (2014)
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Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Advances in neural information processing systems. pp. 6626–6637 (2017)
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2017
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2017
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Incerti, S., Kyriakou, I., Bernal, M., Bordage, M., Francis, Z., Guatelli, S., Ivanchenko, V., Karamitros, M., Lampe, N., Lee, S.B., et al.: Geant4-dna example applications for track structure simulations in liquid water: A report from the geant4-dna project. Medical physics
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Rodríguez, A.C., Kacprzak, T., Lucchi, A., Amara, A., Sgier, R., Fluri, J., Hofmann, T., Réfrégier, A.: Fast cosmic web simulations with generative adversarial networks. Computational Astrophysics and Cosmology
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Deja, K., Dubiński, J., Nowak, P., Wenzel, S., Spurek, P., Trzcinski, T.: End-to-end sinkhorn autoencoder with noise generator. IEEE Access
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Buhmann, E., Diefenbacher, S., Eren, E., Gaede, F., Kasieczka, G., Korol, A., Krüger, K.: Getting high: High fidelity simulation of high granularity calorimeters with high speed. Computing and Software for Big Science
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Kansal, R., Duarte, J., Su, H., Orzari, B., Tomei, T., Pierini, M., Touranakou, M., Vlimant, J.R., Gunopulos, D.: Particle Cloud Generation with Message Passing Generative Adversarial Networks. In: Annual Conference on Neural Information Processing Systems (NeurIPS) (2021)
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Rong, R., Jiang, S., Xu, L., Xiao, G., Xie, Y., Liu, D.J., Li, Q., Zhan, X.: MB-GAN: Microbiome Simulation via Generative Adversarial Network. GigaScience
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Chekalina, V., Orlova, E., Ratnikov, F., Ulyanov, D., Ustyuzhanin, A., Zakharov, E.: Generative models for fast calorimeter simulation: the lhcb case¿. EPJ Web of Conferences
2019
Cited alongside, same era.
Erdmann, M., Glombitza, J., Quast, T.: Precise simulation of electromagnetic calorimeter showers using a wasserstein generative adversarial network. Computing and Software for Big Science
2019
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Dubiński, J., Deja, K., Wenzel, S., Rokita, P., Trzciński, T.: Selectively increasing the diversity of gan-generated samples (2022). https://doi.org/10.48550ARXIV.2207.01561
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