Deep neural operator-driven real-time inference to enable digital twin solutions for nuclear energy systems
K Kobayashi, SB Alam - Scientific reports, 2024 - nature.com
This paper focuses on the feasibility of deep neural operator network (DeepONet) as a
robust surrogate modeling method within the context of digital twin (DT) enabling technology …
robust surrogate modeling method within the context of digital twin (DT) enabling technology …
Functional PCA and deep neural networks-based Bayesian inverse uncertainty quantification with transient experimental data
This work focuses on developing an inverse uncertainty quantification (IUQ) process for time-
dependent responses, using dimensionality reduction by functional principal component …
dependent responses, using dimensionality reduction by functional principal component …
Clustering and uncertainty analysis to improve the machine learning-based predictions of SAFARI-1 control follower assembly axial neutron flux profiles
The goal of this work is to develop accurate Machine Learning (ML) models for predicting
the assembly axial neutron flux profiles in the SAFARI-1 research reactor, trained by …
the assembly axial neutron flux profiles in the SAFARI-1 research reactor, trained by …
ARTISANS—Artificial Intelligence for Simulation of Advanced Nuclear Systems for Nuclear Fission Technology
The objective of this Technical Opinion Paper (TOP) is to provide an overview of the
research topics in the ARTISANS (Artificial Intelligence for Simulation of Advanced Nuclear …
research topics in the ARTISANS (Artificial Intelligence for Simulation of Advanced Nuclear …
Impact of including fuel performance as part of core reload optimization: Application to power uprates
Increasing the power output of existing nuclear power plants, known as performing a power
uprate, could result in improved economic performance, especially with the recent …
uprate, could result in improved economic performance, especially with the recent …
An Investigation on Machine Learning Predictive Accuracy Improvement and Uncertainty Reduction using VAE-based Data Augmentation
The confluence of ultrafast computers with large memory, rapid progress in Machine
Learning (ML) algorithms, and the availability of large datasets place multiple engineering …
Learning (ML) algorithms, and the availability of large datasets place multiple engineering …
Reactor Physics Monitoring of a Source-Driven Subcritical System in Stationary State by Deterministic and Probabilistic Deep Neural Networks
RDE Gatchalian, PV Tsvetkov - Nuclear Science and Engineering, 2024 - Taylor & Francis
Reactivity measurement methods, like the Amplified Source Method (ASM), link observable
quantities to integral physics parameters characterizing subcritical assemblies (SCAs) …
quantities to integral physics parameters characterizing subcritical assemblies (SCAs) …
Study on the off situ reconstruction of the core neutron field based on dual-task hybrid network architecture
P Cao, H Ding, CL Cao, ZH Yang, GM Sun - Nuclear Science and …, 2025 - Springer
The off situ accurate reconstruction of the core neutron field is an important step in realizing
real-time reactor monitoring. The existing off situ reconstruction method of the neutron field is …
real-time reactor monitoring. The existing off situ reconstruction method of the neutron field is …
[PDF][PDF] 基于优化极限学习机模型的反应堆中子通量与k eff 预测方法研究
陈镜宇， 刘喜洋， 赵鹏程， 刘紫静， 李卫 - NUCLEAR …, 2024 - researching.cn
摘要通过模拟和扩展人类智能, 人工智能能够解决预测反应堆keff 和中子通量等问题.
本研究选用国际原子能机构(International Atomic Energy Agency, IAEA) 反应堆作为研究对象 …
本研究选用国际原子能机构(International Atomic Energy Agency, IAEA) 反应堆作为研究对象 …
[图书][B] Machine Learning-Based Prediction of Power at Which Departure from Nucleate Boiling Occurs
CM Godbole - 2023 - search.proquest.com
Departure from nucleate boiling (DNB) is a critical heat flux (CHF) phenomenon typically
seen in pressurized water reactors (PWRs) and is an essential thermal limit for the design …
seen in pressurized water reactors (PWRs) and is an essential thermal limit for the design …