Advancements in digital automation for smart grids have led to the installation of
measurement devices like phasor measurement units (PMUs), micro-PMUs (-PMUs), and …

This paper explores the recent advancements in enhancing Computational Fluid Dynamics
(CFD) tasks through Machine Learning (ML) techniques. We begin by introducing …

Physics-informed neural network (PINN) has been a prevalent framework for solving PDEs
since proposed. By incorporating the physical information into the neural network through …

Graph neural networks (GNNs) have demonstrated significant promise in modelling
relational data and have been widely applied in various fields of interest. The key …

In the past years, the application of neural networks as an alternative to classical numerical
methods to solve Partial Differential Equations has emerged as a potential paradigm shift in …

Nonlinear partial differential equations (PDEs) are used to model dynamical processes in a
large number of scientific fields, ranging from finance to biology. In many applications …

Nonlinear projection equations (NPEs) provide a unified framework for solving various
constrained nonlinear optimization and engineering problems. This paper presents a deep …

Accurately modeling the electrochemical process of large-scale lithium-ion batteries (LLBs),
which involves estimating the electrochemical state distributions within the process, is …

This survey examines the broad suite of methods and models for combining machine
learning with physics knowledge for prediction and forecast, with a focus on partial …

Quantum computing promises to provide the next step up in computational power for diverse
application areas. In this review, we examine the science behind the quantum hype, and the …