Towards robust prediction of material properties for nuclear reactor design under scarce data -- a study in creep rupture property

TL;DR

This paper introduces a meta-learning approach that incorporates uncertainty and prior knowledge to predict material properties reliably in nuclear reactor design, especially when data is scarce, outperforming traditional empirical methods.

Contribution

It presents a novel uncertainty-aware meta-learning framework tailored for limited data scenarios in nuclear material property prediction, enhancing robustness and trustworthiness.

Findings

Achieves superior rupture life prediction accuracy

Demonstrates robustness under small data regimes

Transferable approach applicable to other nuclear industry problems

Abstract

Advances in Deep Learning bring further investigation into credibility and robustness, especially for safety-critical engineering applications such as the nuclear industry. The key challenges include the availability of data set (often scarce and sparse) and insufficient consideration of the uncertainty in the data, model, and prediction. This paper therefore presents a meta-learning based approach that is both uncertainty- and prior knowledge-informed, aiming at trustful predictions of material properties for the nuclear reactor design. It is suited for robust learning under limited data. Uncertainty has been accounted for where a distribution of predictor functions are produced for extrapolation. Results suggest it achieves superior performance than existing empirical methods in rupture life prediction, a case which is typically under a small data regime. While demonstrated herein with rupture properties, this learning approach is transferable to solve similar problems of data scarcity across the nuclear industry. It is of great importance to boosting the AI analytics in the nuclear industry by proving the applicability and robustness while providing tools that can be trusted.