This paper presents a literature review on methods for enabling real-time analysis in digital
twins, which are virtual models of physical systems. The advantages of digital twins are …

In this paper, we adopt conformal prediction, a distribution-free uncertainty quantification
(UQ) framework, to obtain prediction intervals with coverage guarantees for Deep Operator …

This article proposes a model-based deep reinforcement learning (DRL) method to design
emergency control strategies for short-term voltage stability problems in power systems …

Single-operator learning involves training a deep neural network to learn a specific operator,
whereas recent work in multi-operator learning uses an operator embedding structure to …

The dynamic behaviour of a power system can be described by a system of differential–
algebraic equations. Time-domain simulations are used to simulate the evolution of these …

We propose a novel fine-tuning method to achieve multi-operator learning through training a
distributed neural operator with diverse function data and then zero-shot fine-tuning the …

Deep learning has significantly improved the state-of-the-art in computer vision and natural
language processing, and holds great potential to design effective tools for predicting and …

In the pursuit of accurate experimental and computational data while minimizing effort, there
is a constant need for high-fidelity results. However, achieving such results often requires …

The study of operator learning involves the utilization of neural networks to approximate
operators. Traditionally, the focus has been on single-operator learning (SOL). However …

The growing prevalence of inverter-based resources (IBRs) for renewable energy
integration and electrification greatly challenges power system dynamic analysis. To …