Recent breakthroughs in computing power have made it feasible to use machine learning
and deep learning to advance scientific computing in many fields, including fluid mechanics …

Advancements in computing power have recently made it possible to utilize machine
learning and deep learning to push scientific computing forward in a range of disciplines …

Physics informed neural networks (PINNs) are a novel deep learning paradigm primed for
solving forward and inverse problems of nonlinear partial differential equations (PDEs). By …

Herein, two novel Physics Informed Neural Network (PINN) architectures are proposed for
output-only system identification and input estimation of dynamic systems. Using merely …

The present research aims to provide a practical numerical tool for the mechanical analysis
of nanoscale trusses with similar accuracy to molecular dynamics (MD). As a first step, MD …

The intersection of physics and machine learning has given rise to a paradigm that we refer
to here as physics-enhanced machine learning (PEML), aiming to improve the capabilities …

This study explores the implementation of physics-informed neural networks (PINNs) to
analyze turbulent flow in composite porous-fluid systems. These systems are composed of a …

During online implementation of vibration-based structural health monitoring (SHM)
strategies, forward prediction of the system state may allow for improved detection speed …

Stresses resulting from elastic incompatibilities at grain boundaries have long been known
to drive the premature failure and loss of desirable macroscopic properties in polycrystalline …

In engineering design, oftentimes a system's dynamic response is known or can be
measured, but the source generating these responses is not known. The mathematical …