Magic Nature of Neutrons in : First Mass Measurements of S Michimasa, M Kobayashi, Y Kiyokawa, S Ota, DS Ahn, H Baba, ...
Physical review letters 121 (2), 022506, 2018
140 2018 Isomer Decay Spectroscopy of and : Midshell Collectivity Around Z Patel, PA Söderström, Z Podolyák, PH Regan, PM Walker, H Watanabe, ...
Physical review letters 113 (26), 262502, 2014
64 2014 Mapping of a New Deformation Region around S Michimasa, M Kobayashi, Y Kiyokawa, S Ota, R Yokoyama, ...
Physical review letters 125 (12), 122501, 2020
30 2020 Decay spectroscopy of 160Sm: The lightest four-quasiparticle K isomer Z Patel, Z Podolyák, PM Walker, PH Regan, PA Söderström, H Watanabe, ...
Physics Letters B 753, 182-186, 2016
30 2016 Isomer-delayed -ray spectroscopy of midshell nuclei and the variation of -forbidden transition hindrance factors Z Patel, PM Walker, Z Podolyák, PH Regan, TA Berry, PA Söderström, ...
Physical Review C 96 (3), 034305, 2017
19 2017 Leveraging Industry 4.0: Deep Learning, Surrogate Model, and Transfer Learning with Uncertainty Quantification Incorporated into Digital Twin for Nuclear System M Rahman, A Khan, S Anowar, M Al-Imran, R Verma, D Kumar, ...
Handbook of Smart Energy Systems, 1-20, 2022
14 2022 Explainable, interpretable, and trustworthy AI for an intelligent digital twin: A case study on remaining useful life K Kobayashi, SB Alam
Engineering Applications of Artificial Intelligence 129, 107620, 2024
11 2024 Practical Applications of Gaussian Process with Uncertainty Quantification and Sensitivity Analysis for Digital Twin for Accident-Tolerant Fuel K Kobayashi, D Kumar, M Bonney, S Alam
Handbook of Smart Energy Systems, 1-12, 2022
9 2022 Improved generalization with deep neural operators for engineering systems: Path towards digital twin K Kobayashi, J Daniell, SB Alam
Engineering Applications of Artificial Intelligence 131, 107844, 2024
8 * 2024 Data-driven multiscale modeling and robust optimization of composite structure with uncertainty quantification K Kobayashi, S Usman, C Castano, A Alajo, D Kumar, S Naskar, S Alam
Handbook of Smart Energy Systems, 1-11, 2023
8 2023 Physics-informed multi-stage deep learning framework development for digital twin-centred state-based reactor power prediction J Daniell, K Kobayashi, S Naskar, D Kumar, S Chakraborty, A Alajo, ...
arXiv preprint arXiv:2211.13157, 2022
8 2022 Uncertainty quantification and sensitivity analysis for digital twin enabling technology: Application for bison fuel performance code K Kobayashi, D Kumar, M Bonney, S Chakraborty, K Paaren, S Usman, ...
Handbook of Smart Energy Systems, 2265-2277, 2023
7 2023 Surrogate Modeling-Driven Physics-Informed Multi-fidelity Kriging: Path forrward to Digital Twin Enabling Simulation for Accident Tolerant Fuel K Kobayashi, S Usman, C Castano, A Alajo, D Kumar, S Alam
Handbook of Smart Energy Systems, 1-11, 2023
7 2023 Reliability-based robust design optimization method for engineering systems with uncertainty quantification R Verma, D Kumar, K Kobayashi, S Alam
Handbook of Smart Energy Systems, 1-8, 2023
5 2023 Three-quasiparticle isomers in odd-even : Calling for modified spin-orbit interaction for the neutron-rich region R Yokoyama, E Ideguchi, GS Simpson, M Tanaka, Y Sun, CJ Lv, YX Liu, ...
Physical Review C 104 (2), L021303, 2021
4 2021 AI-driven non-intrusive uncertainty quantification of advanced nuclear fuels for digital twin-enabling technology K Kobayashi, D Kumar, SB Alam
Progress in Nuclear Energy 172, 105177, 2024
3 * 2024 Deep neural operator-driven real-time inference to enable digital twin solutions for nuclear energy systems K Kobayashi, SB Alam
Scientific reports 14 (1), 2101, 2024
3 2024 Components of Intelligent Digital Twin Framework for Complex Nuclear Systems K Kobayashi, D James, S Md Nazmus, K Dinesh, A Syed
13th Nuclear Plant Instrumentation, Control & Human-Machine Interface …, 2023
3 2023 Surrogate Modeling-Driven Physics-Informed Multi-fidelity Kriging for the Prediction of Accident-Tolerant Fuel Properties K Kobayashi, S Usman, C Castano, A Alajo, D Kumar, S Alam
Handbook of Smart Energy Systems, 1313-1323, 2023
2 2023 Machine Learning and Artificial Intelligence-Driven Multi-Scale Modeling for High Burnup Accident-Tolerant Fuels for Light Water-Based SMR Applications S Hassan, AH Khan, R Verma, D Kumar, K Kobayashi, S Usman, S Alam
Handbook of Smart Energy Systems, 2131-2154, 2023
2 2023