Reliability Estimation of an Advanced Nuclear Fuel using Coupled Active Learning, Multifidelity Modeling, and Subset Simulation

• URL: http://arxiv.org/abs/2201.02172v1
• Date: Thu, 6 Jan 2022 18:35:56 GMT
• Title: Reliability Estimation of an Advanced Nuclear Fuel using Coupled Active Learning, Multifidelity Modeling, and Subset Simulation
• Authors: Somayajulu L. N. Dhulipala and Michael D. Shields and Promit Chakroborty and Wen Jiang and Benjamin W. Spencer and Jason D. Hales and Vincent M. Laboure and Zachary M. Prince and Chandrakanth Bolisetti and Yifeng Che
• Abstract summary: Tristructural isotropic (TRISO)-coated particle fuel is a robust nuclear fuel and determining its reliability is critical for the success of advanced nuclear technologies. We used coupled active learning, multifidelity modeling, and subset simulation to estimate the failure probabilities of TRISO fuels using several 1D and 2D models.
• Score: 2.0120272257480116
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Tristructural isotropic (TRISO)-coated particle fuel is a robust nuclear fuel and determining its reliability is critical for the success of advanced nuclear technologies. However, TRISO failure probabilities are small and the associated computational models are expensive. We used coupled active learning, multifidelity modeling, and subset simulation to estimate the failure probabilities of TRISO fuels using several 1D and 2D models. With multifidelity modeling, we replaced expensive high-fidelity (HF) model evaluations with information fusion from two low-fidelity (LF) models. For the 1D TRISO models, we considered three multifidelity modeling strategies: only Kriging, Kriging LF prediction plus Kriging correction, and deep neural network (DNN) LF prediction plus Kriging correction. While the results across these multifidelity modeling strategies compared satisfactorily, strategies employing information fusion from two LF models consistently called the HF model least often. Next, for the 2D TRISO model, we considered two multifidelity modeling strategies: DNN LF prediction plus Kriging correction (data-driven) and 1D TRISO LF prediction plus Kriging correction (physics-based). The physics-based strategy, as expected, consistently required the fewest calls to the HF model. However, the data-driven strategy had a lower overall simulation time since the DNN predictions are instantaneous, and the 1D TRISO model requires a non-negligible simulation time.

Related papers

• Practical multi-fidelity machine learning: fusion of deterministic and Bayesian models [0.34592277400656235]
  Multi-fidelity machine learning methods integrate scarce, resource-intensive high-fidelity data with abundant but less accurate low-fidelity data. We propose a practical multi-fidelity strategy for problems spanning low- and high-dimensional domains.
  arXiv  Detail & Related papers  (2024-07-21T10:40:50Z)
• EMR-Merging: Tuning-Free High-Performance Model Merging [55.03509900949149]
  We show that Elect, Mask & Rescale-Merging (EMR-Merging) shows outstanding performance compared to existing merging methods. EMR-Merging is tuning-free, thus requiring no data availability or any additional training while showing impressive performance.
  arXiv  Detail & Related papers  (2024-05-23T05:25:45Z)
• Latent Semantic Consensus For Deterministic Geometric Model Fitting [109.44565542031384]
  We propose an effective method called Latent Semantic Consensus (LSC) LSC formulates the model fitting problem into two latent semantic spaces based on data points and model hypotheses. LSC is able to provide consistent and reliable solutions within only a few milliseconds for general multi-structural model fitting.
  arXiv  Detail & Related papers  (2024-03-11T05:35:38Z)
• Towards Robust and Efficient Cloud-Edge Elastic Model Adaptation via Selective Entropy Distillation [56.79064699832383]
  We establish a Cloud-Edge Elastic Model Adaptation (CEMA) paradigm in which the edge models only need to perform forward propagation. In our CEMA, to reduce the communication burden, we devise two criteria to exclude unnecessary samples from uploading to the cloud.
  arXiv  Detail & Related papers  (2024-02-27T08:47:19Z)
• General multi-fidelity surrogate models: Framework and active learning strategies for efficient rare event simulation [1.708673732699217]
  Estimating the probability of failure for complex real-world systems is often prohibitively expensive. This paper presents a robust multi-fidelity surrogate modeling strategy. It is shown to be highly accurate while drastically reducing the number of high-fidelity model calls.
  arXiv  Detail & Related papers  (2022-12-07T00:03:21Z)
• Closed-form Continuous-Depth Models [99.40335716948101]
  Continuous-depth neural models rely on advanced numerical differential equation solvers. We present a new family of models, termed Closed-form Continuous-depth (CfC) networks, that are simple to describe and at least one order of magnitude faster.
  arXiv  Detail & Related papers  (2021-06-25T22:08:51Z)
• Active Learning with Multifidelity Modeling for Efficient Rare Event Simulation [0.0]
  We propose a framework for active learning with multifidelity modeling emphasizing the efficient estimation of rare events. Our framework works by fusing a low-fidelity (LF) prediction with an HF-inferred correction, filtering the corrected LF prediction to decide whether to call the high-fidelity model. For improved robustness when estimating smaller failure probabilities, we propose using dynamic active learning functions that decide when to call the HF model.
  arXiv  Detail & Related papers  (2021-06-25T17:44:28Z)
• ANNETTE: Accurate Neural Network Execution Time Estimation with Stacked Models [56.21470608621633]
  We propose a time estimation framework to decouple the architectural search from the target hardware. The proposed methodology extracts a set of models from micro- kernel and multi-layer benchmarks and generates a stacked model for mapping and network execution time estimation. We compare estimation accuracy and fidelity of the generated mixed models, statistical models with the roofline model, and a refined roofline model for evaluation.
  arXiv  Detail & Related papers  (2021-05-07T11:39:05Z)
• On Minimum Word Error Rate Training of the Hybrid Autoregressive Transducer [40.63693071222628]
  We study the minimum word error rate (MWER) training of Hybrid Autoregressive Transducer (HAT) From experiments with around 30,000 hours of training data, we show that MWER training can improve the accuracy of HAT models.
  arXiv  Detail & Related papers  (2020-10-23T21:16:30Z)
• Model-Free Voltage Regulation of Unbalanced Distribution Network Based on Surrogate Model and Deep Reinforcement Learning [9.984416150031217]
  This paper develops a model-free approach based on the surrogate model and deep reinforcement learning (DRL) We have also extended it to deal with unbalanced three-phase scenarios.
  arXiv  Detail & Related papers  (2020-06-24T18:49:41Z)
• Hybrid modeling: Applications in real-time diagnosis [64.5040763067757]
  We outline a novel hybrid modeling approach that combines machine learning inspired models and physics-based models. We are using such models for real-time diagnosis applications.
  arXiv  Detail & Related papers  (2020-03-04T00:44:57Z)

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.