14 citations · 23 across the 11 of their papers we have counts for
11 papers
Recent Advances in Uncertainty Quantification Methods for Engineering Problems
Dinesh Kumar, Farid Ahmed, Shoaib Usman +2
In the last few decades, uncertainty quantification (UQ) methods have been used widely to ensure the robustness of engineering designs. This chapter aims to detail recent advances…
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…
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…
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…
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…
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…
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…
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…
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…
Digital Twin and Artificial Intelligence Incorporated With Surrogate Modeling for Hybrid and Sustainable Energy Systems
Abid Hossain Khan, Salauddin Omar, Nadia Mushtary +3
Surrogate modeling has brought about a revolution in computation in the branches of science and engineering. Backed by Artificial Intelligence, a surrogate model can present highly…
Estimating indoor crowd density and movement behavior using WiFi Sensing
Syed Salman Alam, Muhammad Al-Qurishi, Riad Souissi
The fact that almost every person owns a smartphone device that can be precisely located is both empowering and worrying. If methods for accurate tracking of devices (and their own…