3 papers
stat.AP2024
Digital Twin-Centered Hybrid Data-Driven Multi-Stage Deep Learning Framework for Enhanced Nuclear Reactor Power Prediction
James Daniell, Kazuma Kobayashi, Ayodeji Alajo +1
The accurate and efficient modeling of nuclear reactor transients is crucial for ensuring safe and optimal reactor operation. Traditional physics-based models, while valuable, can…
physics.comp-ph2024
A dual physics-informed neural network for topology optimization
Ajendra Singh, Souvik Chakraborty, Rajib Chowdhury
We propose a novel dual physics-informed neural network for topology optimization (DPNN-TO), which merges physics-informed neural networks (PINNs) with the traditional SIMP-based t…
stat.CO2024
AI-driven non-intrusive uncertainty quantification of advanced nuclear fuels for digital twin-enabling technology
Kazuma Kobayashi, Dinesh Kumar, Syed Bahauddin Alam
In response to the urgent need to establish AI/ML-integrated Digital Twin (DT) technology within next-generation nuclear systems, advancements in modeling methods and simulation co…