Explainable, Interpretable & Trustworthy AI for Intelligent Digital Twin: Case Study on Remaining Useful Life

17 January 2023

Papers citing "Explainable, Interpretable & Trustworthy AI for Intelligent Digital Twin: Case Study on Remaining Useful Life"

13 / 13 papers shown

Title
Recent Advances in Uncertainty Quantification Methods for Engineering Problems Dinesh Kumar Farid Ahmed S. Usman A. Alajo S. B. Alam 91 8 0 06 Nov 2022
Uncertainty Quantification and Sensitivity analysis for Digital Twin Enabling Technology: Application for BISON Fuel Performance Code Kazuma Kobayashi Dinesh Kumar M. Bonney S. Chakraborty Kyle Paaren S. B. Alam 27 9 0 14 Oct 2022
Surrogate Modeling-Driven Physics-Informed Multi-fidelity Kriging: Path Forward to Digital Twin Enabling Simulation for Accident Tolerant Fuel Kazuma Kobayashi James Daniell S. Usman Dinesh Kumar S. B. Alam AI4CE 39 7 0 13 Oct 2022
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 Md. Al Imran Richa Verma Dinesh Kumar Kazuma Kobayashi S. B. Alam AI4CE 42 15 0 30 Sep 2022
An Explainable Regression Framework for Predicting Remaining Useful Life of Machines Talhat Khan Kashif Ahmad Jebran Khan Imran Khan Nasir Ahmad 86 13 0 28 Apr 2022
Designing Inherently Interpretable Machine Learning Models Agus Sudjianto Aijun Zhang FaML 31 31 0 02 Nov 2021
Physics-integrated hybrid framework for model form error identification in nonlinear dynamical systems Shailesh Garg S. Chakraborty B. Hazra 66 20 0 01 Sep 2021
Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets Sizhuang He Hanwen Wang P. Perdikaris AI4CE 77 695 0 19 Mar 2021
Explainable AI for Interpretable Credit Scoring Lara Marie Demajo Vince Vella A. Dingli 59 38 0 03 Dec 2020
GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactions Zebin Yang Aijun Zhang Agus Sudjianto FAtt 135 128 0 16 Mar 2020
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators Lu Lu Pengzhan Jin George Karniadakis 216 2,108 0 08 Oct 2019
Metrics for Explainable AI: Challenges and Prospects R. Hoffman Shane T. Mueller Gary Klein Jordan Litman XAI 74 727 0 11 Dec 2018
Model-Agnostic Interpretability of Machine Learning Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 84 838 0 16 Jun 2016