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

• URL: http://arxiv.org/abs/2405.17862v1
• Date: Tue, 28 May 2024 06:20:14 GMT
• Title: Towards robust prediction of material properties for nuclear reactor design under scarce data -- a study in creep rupture property
• Authors: Yu Chen, Edoardo Patelli, Zhen Yang, Adolphus Lye,
• Abstract summary: Key challenges include the availability of data set and insufficient consideration of the uncertainty in the data, model, and prediction. This paper 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.
• Score: 7.068581430279433
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Uncertainties of Latent Representations in Computer Vision [2.33877878310217]
  This thesis makes uncertainty estimates easily accessible by adding them to the latent representation vectors of pretrained computer vision models. We show that these unobservable uncertainties about unobservable latent representations are indeed provably correct.
  arXiv  Detail & Related papers  (2024-08-26T14:02:30Z)
• Uncertainty for Active Learning on Graphs [70.44714133412592]
  Uncertainty Sampling is an Active Learning strategy that aims to improve the data efficiency of machine learning models. We benchmark Uncertainty Sampling beyond predictive uncertainty and highlight a significant performance gap to other Active Learning strategies. We develop ground-truth Bayesian uncertainty estimates in terms of the data generating process and prove their effectiveness in guiding Uncertainty Sampling toward optimal queries.
  arXiv  Detail & Related papers  (2024-05-02T16:50:47Z)
• Physics-constrained Random Forests for Turbulence Model Uncertainty Estimation [0.0]
  We discuss a physics-constrained approach to account for uncertainty of turbulence models. In order to eliminate user input, we incorporate a data-driven machine learning strategy.
  arXiv  Detail & Related papers  (2023-06-23T08:44:56Z)
• Confidence and Dispersity Speak: Characterising Prediction Matrix for Unsupervised Accuracy Estimation [51.809741427975105]
  This work aims to assess how well a model performs under distribution shifts without using labels. We use the nuclear norm that has been shown to be effective in characterizing both properties. We show that the nuclear norm is more accurate and robust in accuracy than existing methods.
  arXiv  Detail & Related papers  (2023-02-02T13:30:48Z)
• The Unreasonable Effectiveness of Deep Evidential Regression [72.30888739450343]
  A new approach with uncertainty-aware regression-based neural networks (NNs) shows promise over traditional deterministic methods and typical Bayesian NNs. We detail the theoretical shortcomings and analyze the performance on synthetic and real-world data sets, showing that Deep Evidential Regression is a quantification rather than an exact uncertainty.
  arXiv  Detail & Related papers  (2022-05-20T10:10:32Z)
• A Sentiment Analysis Dataset for Trustworthiness Evaluation [22.734197353027632]
  Deep learning models are often criticized to be untrustworthy due to the black-box problem. We release a novel and well-annotated sentiment analysis dataset to evaluate robustness and interpretability.
  arXiv  Detail & Related papers  (2021-08-30T11:58:16Z)
• Quantifying Uncertainty for Machine Learning Based Diagnostic [28.499029801148545]
  We use ensemble methods and quantile regression neural networks to explore different ways of creating and analyzing prediction's uncertainty on experimental data from the Linac Coherent Light Source at SLAC. We aim to accurately and confidently predict the current profile or longitudinal phase space images of the electron beam.
  arXiv  Detail & Related papers  (2021-07-29T18:13:01Z)
• Learning Uncertainty For Safety-Oriented Semantic Segmentation In Autonomous Driving [77.39239190539871]
  We show how uncertainty estimation can be leveraged to enable safety critical image segmentation in autonomous driving. We introduce a new uncertainty measure based on disagreeing predictions as measured by a dissimilarity function. We show experimentally that our proposed approach is much less computationally intensive at inference time than competing methods.
  arXiv  Detail & Related papers  (2021-05-28T09:23:05Z)
• Assigning Confidence to Molecular Property Prediction [1.015785232738621]
  Machine learning has emerged as a powerful strategy to learn from existing datasets and perform predictions on unseen molecules. We discuss popular strategies for predicting molecular properties relevant to drug design, their corresponding uncertainty sources and methods to quantify uncertainty and confidence.
  arXiv  Detail & Related papers  (2021-02-23T01:03:48Z)
• Trust but Verify: Assigning Prediction Credibility by Counterfactual Constrained Learning [123.3472310767721]
  Prediction credibility measures are fundamental in statistics and machine learning. These measures should account for the wide variety of models used in practice. The framework developed in this work expresses the credibility as a risk-fit trade-off.
  arXiv  Detail & Related papers  (2020-11-24T19:52:38Z)
• Accurate and Robust Feature Importance Estimation under Distribution Shifts [49.58991359544005]
  PRoFILE is a novel feature importance estimation method. We show significant improvements over state-of-the-art approaches, both in terms of fidelity and robustness.
  arXiv  Detail & Related papers  (2020-09-30T05:29:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.