Zobrazeno 1 - 10
of 22 312
pro vyhledávání: '"Childers"'
Autor:
Hageböck Stephan, Childers Taylor, Hopkins Walter, Mattelaer Olivier, Nichols Nathan, Roiser Stefan, Teig Jørgen, Valassi Andrea, Vuosalo Carl, Wettersten Zenny
Publikováno v:
EPJ Web of Conferences, Vol 295, p 11013 (2024)
Madgraph5_aMC@NLO is one of the most-frequently used MonteCarlo event generators at the LHC, and an important consumer of compute resources. The software has been reengineered to maintain the overall lookand-feel of the user interface while speeding
Externí odkaz:
https://doaj.org/article/f11970bda5d8429880b96165565812a3
Autor:
Spyrou, A., Richman, D., Couture, A., Fields, C. E., Liddick, S. N., Childers, K., Crider, B. P., DeYoung, P. A., Dombos, A. C., Gastis, P., Guttormsen, M., Hermansen, K., Larsen, A. C., Lewis, R., Lyons, S., Midtbø, J. E., Mosby, S., Muecher, D., Naqvi, F., Palmisano-Kyle, A., Perdikakis, G., Prokop, C., Schatz, H., Smith, M. K., Sumithrarachchi, C., Sweet, A.
Publikováno v:
Nature Communications ( 2024) 15:9608
Massive stars are a major source of chemical elements in the cosmos, ejecting freshly produced nuclei through winds and core-collapse supernova explosions into the interstellar medium. Among the material ejected, long lived radioisotopes, such as 60F
Externí odkaz:
http://arxiv.org/abs/2412.01723
While many works have studied statistical data fusion, they typically assume that the various datasets are given in advance. However, in practice, estimation requires difficult data collection decisions like determining the available data sources, th
Externí odkaz:
http://arxiv.org/abs/2411.03195
Autor:
Lersch, Daniel, Schram, Malachi, Dai, Zhenyu, Rajput, Kishansingh, Wu, Xingfu, Sato, N., Childers, J. Taylor
Large scale, inverse problem solving deep learning algorithms have become an essential part of modern research and industrial applications. The complexity of the underlying inverse problem often poses challenges to the algorithm and requires the prop
Externí odkaz:
http://arxiv.org/abs/2407.00051
Autor:
Thelasingha, Neelanga, Julius, Agung, Humann, James, Reddinger, Jean-Paul, Dotterweich, James, Childers, Marshal
This paper presents an iterative planning framework for multi-agent systems with hybrid state spaces. The framework uses transition systems to mathematically represent planning tasks and employs multiple solvers to iteratively improve the plan until
Externí odkaz:
http://arxiv.org/abs/2401.08846