As predictive analytical tools, artificial intelligence (AI) and machine learning (ML) show promise in improving nuclear reactor safety while offering economic savings. To get a better understanding of current usage and future trends in AI and ML in the commercial nuclear power industry, the Nuclear Regulatory Commission is seeking comments from the public, the nuclear industry, and other stakeholders, as well as other interested individuals and organizations.

The Department of Energy on August 19 announced several awards to research teams applying artificial intelligence and machine learning to fusion energy. The planned total funding of $21 million is targeted at projects with time frames of up to three years; $8 million in fiscal year 2020 funding has already been committed to the work. Delivery of the balance-of-project funding will depend on future congressional appropriations.

“These awards will enable fusion researchers to take advantage of recent rapid advances in artificial intelligence and machine learning,” said Chris Fall, director of the DOE’s Office of Science. “AI and ML will help us to accelerate progress in fusion and keep American scientists at the forefront of fusion research.”

On August 4, the Department of Energy announced it will provide $57.5 million over five years to establish two multidisciplinary teams to take advantage of DOE supercomputing facilities at Argonne National Laboratory, Lawrence Berkeley National Laboratory, and Oak Ridge National Laboratory. The goal is to spur advances in the use of artificial intelligence and machine learning. Funds of $11.5 million have been made available for Fiscal Year 2020, with future funding contingent on congressional appropriations.

A quantum computer, such as this 50-bit version that IBM demonstrated at the International Consumer Electronics Show in 2018, is capable of solving tasks inaccessible to the most powerful “classic ” supercomputer. (Photo: IBM)

Rosatom, Russia’s state atomic energy corporation, and the Russian Quantum Center (RQC) on July 7 announced the creation of the first laboratory in Russia to research and develop machine learning and artificial intelligence (AI) methods on quantum computers, specializing in the application of these technologies in the nuclear industry. An agreement was signed between the RQC and Tsifrum, a Rosatom subsidiary that was created in 2019 to support the implementation of Rosatom’s digitalization strategy.

We often define noise as an unwanted disturbance, especially acoustic in nature. Neutron noise, by contrast, is a direct measure of the dynamics of a nuclear core. It can be used for core monitoring without disturbing plant operation and by using the existing core instrumentation. The European CORTEX project aims to develop an innovative core monitoring technique using neutron noise, while capitalizing on the latest developments in neutronic modeling, signal processing, and artificial intelligence.

The Department of Energy announced on March 4 that it will provide $30 million for new research on fusion energy. The funding will provide $17 million for research focused specifically on artificial intelligence (AI) and machine learning (ML) approaches for the prediction of key plasma phenomena, management of facility operations, and accelerated discovery through data science, among other topics. An additional $13 million under a separate funding opportunity will be devoted to fundamental fusion theory research, including computer modeling and simulation, focused on factors affecting the behavior of hot plasmas confined by magnetic fields in fusion reactors.