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Reactor Physics
The division's objectives are to promote the advancement of knowledge and understanding of the fundamental physical phenomena characterizing nuclear reactors and other nuclear systems. The division encourages research and disseminates information through meetings and publications. Areas of technical interest include nuclear data, particle interactions and transport, reactor and nuclear systems analysis, methods, design, validation and operating experience and standards. The Wigner Award heads the awards program.
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ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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Nuclear News 40 Under 40 discuss the future of nuclear
Seven members of the inaugural Nuclear News 40 Under 40 came together on March 4 to discuss the current state of nuclear energy and what the future might hold for science, industry, and the public in terms of nuclear development.
To hear more insights from this talented group of young professionals, watch the “40 Under 40 Roundtable: Perspectives from Nuclear’s Rising Stars” on the ANS website.
François Bachoc, Karim Ammar, Jean-Marc Martinez
Nuclear Science and Engineering | Volume 183 | Number 3 | July 2016 | Pages 387-406
Technical Paper | doi.org/10.13182/NSE15-108
Articles are hosted by Taylor and Francis Online.
It is now common practice in nuclear engineering to base extensive studies on numerical computer models. These studies require running computer codes in potentially thousands of numerical configurations and without expert individual controls on the computational and physical aspects of each simulation. In this paper, we compare different statistical metamodeling techniques and show how metamodels can help improve the global behavior of codes in these extensive studies. We consider the metamodeling of the Germinal thermomechanical code by Kriging, kernel regression, and neural networks. Kriging provides the most accurate predictions, while neural networks yield the fastest metamodel functions. All three metamodels can conveniently detect strong computation failures. However, it is more challenging to detect code instabilities, that is, groups of computations that are all valid but numerically inconsistent with one another. For code instability detection, we find that Kriging provides an interesting tool.