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Deep Neural Operator Driven Real Time Inference for Nuclear Systems to Enable Digital Twin Solutions
This study marks an important step towards harnessing the power of surrogate modeling techniques in critical engineering domains.
Improved generalization with deep neural operators for engineering systems: Path towards digital twin
Neural Operator Networks (ONets) represent a novel advancement in machine learning algorithms, offering a robust and generalizable alternative for approximating partial differential equations (PDEs) solutions.
Explainable, Interpretable & Trustworthy AI for Intelligent Digital Twin: Case Study on Remaining Useful Life
Therefore, the use of explainable AI (XAI) and interpretable machine learning (IML) is crucial for the accurate prediction of prognostics, such as remaining useful life (RUL), in a digital twin system, to make it intelligent while ensuring that the AI model is transparent in its decision-making processes and that the predictions it generates can be understood and trusted by users.
Decision Making
Explainable Artificial Intelligence (XAI)
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