124 citations · 282 across the 36 of their papers we have counts for
18 papers · 1 filter
Probabilistic machine learning based predictive and interpretable digital twin for dynamical systems
Tapas Tripura, Aarya Sheetal Desai, Sondipon Adhikari +1
A framework for creating and updating digital twins for dynamical systems from a library of physics-based functions is proposed. The sparse Bayesian machine learning is used to upd…
MAntRA: A framework for model agnostic reliability analysis
Yogesh Chandrakant Mathpati, Kalpesh Sanjay More, Tapas Tripura +2
We propose a novel model agnostic data-driven reliability analysis framework for time-dependent reliability analysis. The proposed approach -- referred to as MAntRA -- combines int…
Model-agnostic stochastic model predictive control
Tapas Tripura, Souvik Chakraborty
We propose a model-agnostic stochastic predictive control (MASMPC) algorithm for dynamical systems. The proposed approach first discovers \textit{interpretable} governing different…
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…