ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
Explore the many uses for nuclear science and its impact on energy, the environment, healthcare, food, and more.
Division Spotlight
Thermal Hydraulics
The division provides a forum for focused technical dialogue on thermal hydraulic technology in the nuclear industry. Specifically, this will include heat transfer and fluid mechanics involved in the utilization of nuclear energy. It is intended to attract the highest quality of theoretical and experimental work to ANS, including research on basic phenomena and application to nuclear system design.
Meeting Spotlight
Utility Working Conference and Vendor Technology Expo (UWC 2024)
August 4–7, 2024
Marco Island, FL|JW Marriott Marco Island
Standards Program
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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Jul 2024
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Nuclear Science and Engineering
August 2024
Nuclear Technology
Fusion Science and Technology
Latest News
ARPA-E announces $40 million to develop transmutation technologies for UNF
The Department of Energy’s Advanced Research Projects Agency–Energy (ARPA-E) announced $40 million in funding to develop cutting-edge technologies to enable the transmutation of used nuclear fuel into less-radioactive substances. According to ARPA-E, the new initiative addresses one of the agency’s core goals as outlined by Congress: to provide transformative solutions to improve the management, cleanup, and disposal of radioactive waste and spent nuclear fuel.
Laura Laghi, Enrico Schiassi, Mario De Florio, Roberto Furfaro, Domiziano Mostacci
Nuclear Science and Engineering | Volume 197 | Number 9 | September 2023 | Pages 2373-2403
Research Article | doi.org/10.1080/00295639.2022.2160604
Articles are hosted by Taylor and Francis Online.
This work aims to solve six problems with four different physics-informed machine learning frameworks and compare the results in terms of accuracy and computational cost. First, we considered the diffusion-advection-reaction equations, which are second-order linear differential equations with two boundary conditions. The first algorithm is the classic Physics-Informed Neural Networks. The second one is Physics-Informed Extreme Learning Machine. The third framework is Deep Theory of Functional Connections, a multilayer neural network based on the solution approximation via a constrained expression that always analytically satisfies the boundary conditions. The last algorithm is the Extreme Theory of Functional Connections (X-TFC), which combines Theory of Functional Connections and shallow neural network with random features [e.g., Extreme Learning Machine (ELM)]. The results show that for these kinds of problems, ELM-based frameworks, especially X-TFC, overcome those using deep neural networks both in terms of accuracy and computational time.