# Digital-Twin-Based Improvements to Diagnosis, Prognosis, Strategy Assessment, and Discrepancy Checking in a Nearly Autonomous Management and Control System

arXiv: https://arxiv.org/abs/2105.11039 · HTML: https://mlsanity.com/paper/2105.11039/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 44 references that are in the map.
- First posted on arXiv 23 May 2021.

## Builds on (most cited first, 3 of 3)

- [Development and Assessment of a Nearly Autonomous Management and Control System for Advanced Reactors](https://mlsanity.com/paper/2008.12888/) (2020)
- [Uncertainty Quantification and Software Risk Analysis for Digital Twins in the Nearly Autonomous Management and Control Systems: A Review](https://mlsanity.com/paper/2103.03680/) (2021)
- [Predictive Capability Maturity Quantification using Bayesian Network](https://mlsanity.com/paper/2010.03373/) (2020)

## Built on by (most cited first, 1 of 1)

- [Quantification of Deep Neural Network Prediction Uncertainties for VVUQ of Machine Learning Models](https://mlsanity.com/paper/2206.14615/) (2022)

## Abstract (arXiv)

The Nearly Autonomous Management and Control System (NAMAC) is a comprehensive control system that assists plant operations by furnishing control recommendations to operators in a broad class of situations. This study refines a NAMAC system for making reasonable recommendations during complex loss-of-flow scenarios with a validated Experimental Breeder Reactor II simulator, digital twins improved by machine-learning algorithms, a multi-attribute decision-making scheme, and a discrepancy checker for identifying unexpected recommendation effects. We assessed the performance of each NAMAC component, while we demonstrated and evaluated the capability of NAMAC in a class of loss-of-flow scenarios.