PSAM 16 Conference Paper Overview
Welcome to the PSAM 16 Conference paper and speaker overview page.
Lead Author: Junyong Bae Co-author(s): Jong Woo Park, jongwoo822@unist.ac.kr
Seung Jun Lee, sjlee420@unist.ac.kr
| Deep learning for Guided Simulation of Scenarios for Dynamic Probabilistic Risk Assessment |
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| One of the practical challenges of simulation-based dynamic risk assessment is to optimize a large number of scenarios that should be analyzed by computationally expensive codes such as thermal-hydraulic system codes. To tackle this challenge, this research suggests a guided simulation framework inspired by the human reasoning process utilizing deep learning. This framework employs a deep neural network to estimate the consequences of assumed scenarios based on the result obtained from the simulated scenarios and quantifies the estimation confidence using Monte Carlo dropout. In addition, an autoencoder and a mean-shift clustering are implemented to group long sequential records of simulation results. As a result, this framework can point out the scenarios that should be analyzed preferentially. This consequence-based optimizing framework could be applied as a scenario screening engine for an advanced dynamic risk assessment framework, alongside a probability-based optimizing framework. |
Paper JU184 Preview
Author and Presentation Info
Lead Author Name: Junyong Bae (junyong8090@unist.ac.kr)
Bio: Junyong Bae is Ph.d candidate from Ulsan national institute of science and technology (UNIST) in Nuclear Engineering. His research interest is an application of deep-learning to enhance the safey of nuclear power plant.
Country: South Korea
Company: Ulsan National Institute of Science and Technology
Job Title: Graduate Student
Bio: Junyong Bae is Ph.d candidate from Ulsan national institute of science and technology (UNIST) in Nuclear Engineering. His research interest is an application of deep-learning to enhance the safey of nuclear power plant.
Country: South Korea
Company: Ulsan National Institute of Science and Technology
Job Title: Graduate Student