The rapid growth of deep learning research, including within the field of computational
mechanics, has resulted in an extensive and diverse body of literature. To help researchers …

Abstract Machine learning (ML) has evolved as a technology used in even broader domains,
ranging from spam detection to space exploration, as a result of the boom in available data …

One of the most promising applications of machine learning in computational physics is to
accelerate the solution of partial differential equations (PDEs). The key objective of machine …

Exploring nanoscale material properties through light-matter interactions is essential to
unveil new phenomena and manipulate materials at the atomic level, paving the way for …

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 …

Physics-informed neural networks (PINNs) have recently become a viable modelling method
for the scientific machine-learning community. The appeal of this network architecture lies in …

While significant progress has been made on Physics-Informed Neural Networks (PINNs), a
comprehensive comparison of these methods across a wide range of Partial Differential …

Recent years have witnessed a growth in mathematics for deep learning—which seeks a
deeper understanding of the concepts of deep learning with mathematics and explores how …

Nanophotonic devices manipulate light at sub-wavelength scales, enabling tasks such as
light concentration, routing, and filtering. Designing these devices to achieve precise light …

Physics-informed neural networks (PINNs) have recently become a new popular method for
solving forward and inverse problems governed by partial differential equations. However, in …