Probabilistic deep learning model as a tool for supporting the fast simulation of a thermal–hydraulic code
作者：
Highlights：
• Reducing uncertainty in probabilistic safety assessment strengthens nuclear safety.
• For massive simulations to reduce uncertainty, a fast-running model is necessary.
• A deep learning-based surrogate model of thermal–hydraulic (TH) code is proposed.
• The proposed eQRNN estimates the results of TH code with their uncertain boundary.
• Fast and probabilistic estimation of TH code results can improve nuclear safety.
摘要
•Reducing uncertainty in probabilistic safety assessment strengthens nuclear safety.•For massive simulations to reduce uncertainty, a fast-running model is necessary.•A deep learning-based surrogate model of thermal–hydraulic (TH) code is proposed.•The proposed eQRNN estimates the results of TH code with their uncertain boundary.•Fast and probabilistic estimation of TH code results can improve nuclear safety.
论文关键词：Deep learning,LSTM,Surrogate model,Thermal-hydraulic code,Recurrent neural networks,Quantile regression,Positional encoding
论文评审过程：Received 10 May 2021, Revised 10 November 2021, Accepted 20 March 2022, Available online 24 March 2022, Version of Record 30 March 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.116966
