The recent development of machine learning (ML) and Deep Learning (DL) increases the
opportunities in all the sectors. ML is a significant tool that can be applied across many …

Over the last three decades, fiber optic sensors (FOS) have gained a lot of attention for their
wide range of monitoring applications across many industries, including aerospace …

Deep neural operators can learn nonlinear mappings between infinite-dimensional function
spaces via deep neural networks. As promising surrogate solvers of partial differential …

This review discusses Operator Inference, a nonintrusive reduced modeling approach that
incorporates physical governing equations by defining a structured polynomial form for the …

Abstract Physics-Informed Neural Networks (PINNs) have emerged recently as a promising
application of deep neural networks to the numerical solution of nonlinear partial differential …

Deep learning (DL) has had unprecedented success and is now entering scientific
computing with full force. However, current DL methods typically suffer from instability, even …

Highly accurate numerical or physical experiments are often very time-consuming or
expensive to obtain. When time or budget restrictions prohibit the generation of additional …

Abstract Machine learning models have been successfully used in many scientific and
engineering fields. However, it remains difficult for a model to simultaneously utilize domain …

Solving power flow (PF) equations is the basis of power flow analysis, which is important in
determining the best operation of existing systems, performing security analysis, etc …

Early results have shown the potential of Deep Learning (DL) to disrupt the fields of optical
inverse-design, particularly, the inverse design of nanostructures. In the last three years, the …