TY - GEN
T1 - Space Weather modeling at the University of Colorado Deep Learning Laboratory
AU - Camporeale, E.
AU - Hu, A.
AU - Lucas, G.
AU - Knuth, J.
AU - Berger, T.
N1 - Publisher Copyright:
© 2024 IEEE. All rights reserved.
PY - 2024
Y1 - 2024
N2 - Our team at the University of Colorado, Boulder, has undertaken significant strides in the development of cutting-edge models within the realm of space weather and space physics, leveraging a state-of-the-art Deep Learning Laboratory with approximately 312 Tflops in single precision—an outcome of substantial investments in AI/ML.
AB - Our team at the University of Colorado, Boulder, has undertaken significant strides in the development of cutting-edge models within the realm of space weather and space physics, leveraging a state-of-the-art Deep Learning Laboratory with approximately 312 Tflops in single precision—an outcome of substantial investments in AI/ML.
UR - https://www.scopus.com/pages/publications/85190142776
U2 - 10.23919/USNC-URSINRSM60317.2024.10465097
DO - 10.23919/USNC-URSINRSM60317.2024.10465097
M3 - Conference contribution
AN - SCOPUS:85190142776
T3 - 2024 United States National Committee of URSI National Radio Science Meeting, USNC-URSI NRSM 2024 - Proceedings
SP - 342
BT - 2024 United States National Committee of URSI National Radio Science Meeting, USNC-URSI NRSM 2024 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 United States National Committee of URSI National Radio Science Meeting, USNC-URSI NRSM 2024
Y2 - 9 January 2024 through 12 January 2024
ER -