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Conference Spotlight
2025 ANS Winter Conference & Expo
November 9–12, 2025
Washington, DC|Washington Hilton
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Fusion Science and Technology
October 2025
Latest News
DOE’s latest fusion energy road map aims to bridge known gaps
The Department of Energy introduced a Fusion Science & Technology (S&T) Roadmap on October 16 as a national “Build–Innovate–Grow” strategy to develop and commercialize fusion energy by the mid-2030s by aligning public investment and private innovation. Hailed by Darío Gil, the DOE’s new undersecretary for science, as bringing “unprecedented coordination across America's fusion enterprise” and advancing President Trump’s January 2025 executive order, on “Unleashing American Energy,” the road map echoes plans issued by the DOE’s Office of Fusion Energy Sciences (FES) in 2023 and 2024, with a new emphasis on the convergence of AI and fusion.
The road map release coincided with other fusion energy events held this week in Washington, D.C., and beyond.
Bhavya Reddy, Ezgi Gursel, Katy Daniels, Anahita Khojandi, Jamie Baalis Coble, Vivek Agarwal, Ronald Boring, Vaibhav Yadav, Mahboubeh Madadi
Nuclear Technology | Volume 210 | Number 12 | December 2024 | Pages 2312-2330
Research Article | doi.org/10.1080/00295450.2024.2372217
Articles are hosted by Taylor and Francis Online.
The timely and accurate identification of incidents, such as human factor error, is important to restore nuclear power plants (NPPs) to a stable state. However, the identification of abnormal operating conditions is difficult because of the existence of multiple scenarios. In addition, to implement mitigation actions rapidly after an incident occurs, operators must accurately identify an incident by monitoring the trends of many variables. The mental burden posed by this can increase human error and cause failure in identifying incidents. Failure to identify incidents directly results in erroneous mitigation measures, which are detrimental to NPPs.
In this study, we leverage uncertainty-aware models to identify such errors and thereby increase the chances of mitigating them. We use the data collected from a physical test bed. The goal is to identify both certain and accurate models. For this, the two main aspects of focus in this study are explainable artificial intelligence (XAI) and uncertainty quantification (UQ). While XAI elucidates the decision pathway, UQ evaluates decision reliability. Their integration paints a comprehensive picture, signifying that understanding decisions and their confidence should be interlinked.
Thus, in this study we leverage UQ measures (e.g. entropy and mutual information) along with Shapley additive explanations to gain insights into the features contributing to both accuracy and uncertainty in error identification. Our results show that uncertainty-aware models combined with XAI tools can explain the artificial intelligence–prescribed decisions, with the potential of better explaining errors for the operators.