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Denver, CO|Sheraton Denver
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On moving fast and breaking things
Craig Piercycpiercy@ans.org
So much of what is happening in federal nuclear policy these days seems driven by a common approach popularized in the technology sector. Silicon Valley calls it “move fast and break things,” a phrase originally associated with Facebook’s early culture under Mark Zuckerberg. The idea emerged in the early 2000s as software companies discovered that rapid iteration, frequent experimentation, and a willingness to tolerate failure could dramatically accelerate innovation. This philosophy helped drive the growth of the social media, smartphones, cloud computing, and digital platforms that now underpin modern economic and social life.
Today, that mindset is also influencing federal nuclear policy. The Trump administration views accelerated nuclear deployment as part of a broader competition with China for technological and AI leadership. In that context, it seems willing to accept greater operational risk in pursuit of strategic advantage and long-term economic and security objectives.
12th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies (NPIC&HMIT 2021)
Technical Session
Wednesday, June 16, 2021|4:30–6:15PM EDT
Session Chair:
Brent Shumaker (Analysis and Measurement Services Corp)
Alternate Chair:
Jamie B. Coble (University of Tennessee-Knoxville)
Session Organizer:
Staff Producer:
Janet Davis (ANS)
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Physics-Based Automated Reasoning for Health Monitoring: Sensor Set Selection
R. Vilim (ANL), T. Nguyen (ANL), R. Ponciroli (ANL), H. Wang (ANL)
Paper
Reference
A Parallel Capability Using Genetic Algorithm for Sensor Assignment Optimization with Process-Constrained Data-Analytic Diagnosis
Yuxuan Liu (Univ. of Michigan), Brendan Kochunas (Univ. of Michigan), Tat Nghia Nguyen (ANL), Hubert Ley (ANL), Richard Vilim (ANL)
A Model-Based Symbolic Inference for Sensor Deployment Optimization for Fault Detection of the EBR-II Reactor
Xiaoxu Diao (The Ohio State Univ.), Pavan Kumar Vaddi (The Ohio State Univ.), Boyuan Li (The Ohio State Univ.), Wei Gao (The Ohio State Univ.), Carol Smidts (The Ohio State Univ.)
Physics-Informed Machine Learning-Aided System Space Discretization
Junyung Kim (Rensselaer Polytechnic Institute), Asad Ullah Amin Shah (Rensselaer Polytechnic Institute), Hyun Gook Kang (Rensselaer Polytechnic Institute), Xingang Zhao (ORNL)
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