most citedMachine Learning and Artificial Intelligence-Driven Multi-Scale Modeling for High Burnup Accident-Tolerant Fuels for Light Water-Based SMR Applications

3 citations · 6 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CY20221 cited

Data-driven multi-scale modeling and robust optimization of composite structure with uncertainty quantification

Kazuma Kobayashi, Shoaib Usman, Carlos Castano +2

It is important to accurately model materials' properties at lower length scales (micro-level) while translating the effects to the components and/or system level (macro-level) can…

stat.CO2022

Surrogate Modeling-Driven Physics-Informed Multi-fidelity Kriging: Path Forward to Digital Twin Enabling Simulation for Accident Tolerant Fuel

Kazuma Kobayashi, James Daniell, Shoaib Usman +2

The Gaussian Process (GP)-based surrogate model has the inherent capability of capturing the anomaly arising from limited data, lack of data, missing data, and data inconsistencies…

stat.AP20221 cited

Uncertainty Quantification and Sensitivity analysis for Digital Twin Enabling Technology: Application for BISON Fuel Performance Code

Kazuma Kobayashi, Dinesh Kumar, Matthew Bonney +3

To understand the potential of intelligent confirmatory tools, the U.S. Nuclear Regulatory Committee (NRC) initiated a future-focused research project to assess the regulatory viab…

stat.CO20221 cited

Reliability-Based Robust Design Optimization Method for Engineering Systems with Uncertainty Quantification

Richa Verma, Dinesh Kumar, Kazuma Kobayashi +1

Robust optimization is a method for optimization under uncertainties in engineering systems and designs for applications ranging from aeronautics to nuclear. In a robust design pro…

physics.med-ph2022

Practical Applications of Gaussian Process with Uncertainty Quantification and Sensitivity Analysis for Digital Twin for Accident Tolerant Fuel

Kazuma Kobayashi, Dinesh Kumar, Matthew Bonney +1

The application of digital twin (DT) technology to the nuclear field is one of the challenges in the future development of nuclear energy. Possible applications of DT technology in…

cs.LG2022

Leveraging Industry 4.0 -- Deep Learning, Surrogate Model and Transfer Learning with Uncertainty Quantification Incorporated into Digital Twin for Nuclear System

M. Rahman, Abid Khan, Sayeed Anowar +5

Industry 4.0 targets the conversion of the traditional industries into intelligent ones through technological revolution. This revolution is only possible through innovation, optim…