The advances of deep learning (DL) techniques bring new opportunities to numerous
intractable tasks in power systems (PSs). Nevertheless, the extension of the application of …

Modern power grids are fast evolving with the increasing volatile renewable generation,
distributed energy resources (DERs) and time-varying operating conditions. The DERs …

Discovering mathematical equations that govern physical and biological systems from
observed data is a fundamental challenge in scientific research. We present a new physics …

Predictive maintenance for power electronic converters has emerged as a critical area of
research and development. With the rapid advancements in deep-learning techniques, new …

Develo** accurate dynamic models for various systems is crucial for optimization, control,
fault diagnosis, and prognosis. Recent advancements in information technologies and …

Data-driven approaches coupled with physical knowledge are powerful techniques to model
engineering systems. The goal of such models is to efficiently solve for the underlying …

In the context of industrial automation and smart manufacturing, the necessity to optimize the
performance of high-power induction furnaces (IFs) has increased considerably. Operational …

Constructing first-principles models is usually a challenging and time-consuming task due to
the complexity of real-life processes. On the other hand, data-driven modeling, particularly a …

We introduce a new method for solving parametric heat transfer partial differential equations
on evolving geometries in advanced manufacturing applications. Physics-informed neural …

Ordinary and partial differential equations (DE) are used extensively in scientific and
mathematical domains to model physical systems. Current literature has focused primarily …