Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Finn H. O'Shea"'
Publikováno v:
APL Machine Learning, Vol 1, Iss 2, Pp 026102-026102-7 (2023)
Fusion power production in tokamaks uses discharge configurations that risk producing strong type I edge localized modes. The largest of these modes will likely increase impurities in the plasma and potentially damage plasma facing components, such a
Externí odkaz:
https://doaj.org/article/54be659cb040439bbadabeccc66bb7eb
Autor:
Andreas Wituschek, Lukas Bruder, Enrico Allaria, Ulrich Bangert, Marcel Binz, Roberto Borghes, Carlo Callegari, Giulio Cerullo, Paolo Cinquegrana, Luca Giannessi, Miltcho Danailov, Alexander Demidovich, Michele Di Fraia, Marcel Drabbels, Raimund Feifel, Tim Laarmann, Rupert Michiels, Najmeh Sadat Mirian, Marcel Mudrich, Ivaylo Nikolov, Finn H. O’Shea, Giuseppe Penco, Paolo Piseri, Oksana Plekan, Kevin Charles Prince, Andreas Przystawik, Primož Rebernik Ribič, Giuseppe Sansone, Paolo Sigalotti, Simone Spampinati, Carlo Spezzani, Richard James Squibb, Stefano Stranges, Daniel Uhl, Frank Stienkemeier
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-7 (2020)
Light pulses with controllable parameters are desired for studying the fundamental properties of matter. Here the authors generate and use phase-manipulated and highly time-stable XUV pulse pairs to probe the coherent evolution and dephasing of XUV e
Externí odkaz:
https://doaj.org/article/60e3b9f7f18845708f5532a512019378
Autor:
Ryan Humble, Finn H. O’Shea, William Colocho, Matt Gibbs, Helen Chaffee, Eric Darve, Daniel Ratner
Publikováno v:
Physical Review Accelerators and Beams, Vol 25, Iss 12, p 122804 (2022)
Accelerators produce too many signals for a small operations team to monitor in real time. In addition, many of these signals are only interpretable by subject matter experts with years of experience. As a result, changes in accelerator performance c
Externí odkaz:
https://doaj.org/article/c44598e5a5ad480899d82981fd676830
Publikováno v:
Communications in Computer and Information Science ISBN: 9783031236051
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5273e33b73017a115c8304c7345b4f94
https://doi.org/10.1007/978-3-031-23606-8_7
https://doi.org/10.1007/978-3-031-23606-8_7
Publikováno v:
Machine Learning: Science and Technology, Vol 5, Iss 3, p 035050 (2024)
Using supervised learning to train a machine learning model to predict an on-coming edge localized mode (ELM) requires a large number of labeled samples. Creating an appropriate data set from the very large database of discharges at a long-running to
Externí odkaz:
https://doaj.org/article/a9b0cefb23d840e99864d3b77e82d7fc