Nuclear technology industries have increased their interest in using data-driven methods to
improve safety, reliability, and availability of assets. To do so, it is important to understand …

Soft Computing (SC) is defined as a group of computational techniques that solve complex
problems independent of mathematical models. SC techniques including artificial neural …

We present an open-source Python framework for NeuroEvolution Optimization with
Reinforcement Learning (NEORL) developed at the Massachusetts Institute of Technology …

In this paper the use of machine learning (ML) is explored as an efficient tool for uncertainty
quantification. A machine learning algorithm is developed to predict the peak cladding …

We combine advances in deep reinforcement learning (RL) with evolutionary computation to
perform large-scale optimisation of boiling water reactor (BWR) bundles using …

In order to maximize both the life cycle and efficiency of a reactor core, it is essential to find
the optimum loading pattern. In the case of research reactors, a loading pattern can also be …

Critical heat flux (CHF) is an essential parameter that plays a significant role in ensuring the
safety and economic efficiency of nuclear power facilities. It imposes design and operational …

In this paper, we develop a novel optimization algorithm, Bat Algorithm (BA), in order to
implement in the Loading Pattern Optimization (LPO) of nuclear reactor core. For performing …

The pattern of the core reload program is very important for an optimize use of research
reactors. Reactor safety issues and economic efficiency should be considered during pattern …

A Multi-State Recurrent Neural Network is used to optimize Loading Patterns (LP) in BWRs.
We have proposed an energy function that depends on fuel assembly positions and their …