DIFFER

A. Ho

First name
A.
Last name
Ho
ORCID
0000-0001-5107-3531
Maggi, C. ., Abate, D. ., van Berkel, M. ., Bosman, T. ., Ceelen, L. ., Derks, G. ., … al., et . (2024). Overview of T and D-T results in JET with ITER-like wall. Nuclear Fusion, 64(11), 112012. https://doi.org/10.1088/1741-4326/ad3e16
Panera Alvarez, A. ., Ho, A. ., Jarvinen, A. ., Saarelma, S. ., Wiesen, S. ., Contributors, J. ., & Team, A. U. (2024). EuroPED-NN: uncertainty aware surrogate model. Plasma Physics and Controlled Fusion, 66(9), 095012. https://doi.org/10.1088/1361-6587/ad6707
Wiesen, S. ., Dasbach, S. ., Kit, A. ., Jarvinen, A. ., Gillgren, A. ., Ho, A. ., … Strand, P. . (2024). Data-driven models in fusion exhaust: AI methods and perspectives. Nuclear Fusion, 64(8), 086046. https://doi.org/10.1088/1741-4326/ad5a1d
Zanisi, L. ., Ho, A. ., Barr, J. ., Madula, T. ., Citrin, J. ., Pamela, S. ., … Gopakumar, V. . (2024). Efficient training sets for surrogate models of tokamak turbulence with Active Deep Ensembles. Nuclear Fusion, 64(3), 036022. https://doi.org/10.1088/1741-4326/ad240d
Fransson, E. ., Gillgren, A. ., Ho, A. ., Borsander, J. ., Lindberg, O. ., Rieck, W. ., … Strand, P. . (2023). A fast neural network surrogate model for the eigenvalues of QuaLiKiz. Physics of Plasmas, 30(12), 123904. https://doi.org/10.1063/5.0174643
Solano, E. ., Birkenmeier, G. ., Silva, C. ., Delabie, E. ., Hillesheim, J. ., Baciero, A. ., … Contributors, J. . (2023). L-H transition studies in tritium and deuterium–tritium campaigns at JET with Be wall and W divertor. Nuclear Fusion, 63(11), 112011. https://doi.org/10.1088/1741-4326/acee12
Garcia, J. ., Casson, F. ., Frassinetti, L. ., Gallart, D. ., Garzotti, L. ., Kim, H. ., … Contributors, J. . (2023). Modelling performed for predictions of fusion power in JET DTE2: overview and lessons learnt. Nuclear Fusion, 63(11), 112003. https://doi.org/10.1088/1741-4326/acedc0
Kim, H. ., Auriemma, F. ., Ferreira, J. ., Gabriellini, S. ., Ho, A. ., Huynh, P. ., … Contributors, J. . (2023). Validation of D-T fusion power prediction capability against 2021 JET D-T experiments. Nuclear Fusion, 63(11), 112004. https://doi.org/10.1088/1741-4326/ace26d
Hobirk, J. ., Challis, C. ., Kappatou, A. ., Lerche, E. ., Keeling, D. ., King, D. ., … Contributors, J. . (2023). The JET hybrid scenario in Deuterium, Tritium and Deuterium-Tritium. Nuclear Fusion, 63(11), 112001. https://doi.org/10.1088/1741-4326/acde8d
Bosman, T. ., Kochl, F. ., Ho, A. ., de Baar, M. ., Krishnamoorthy, D. ., & van Berkel, M. . (2023). Integrated model control simulations of the electron density profile and the implications of using multiple hybrid pellet injectors for control. Nuclear Fusion, 63(12), 126047. https://doi.org/10.1088/1741-4326/ad0251