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

Physics-informed neural networks (PINNs) represent a significant advancement at the
intersection of machine learning and physical sciences, offering a powerful framework for …

This paper presents a physics-informed neural network (PINN) approach for monitoring the
health of diesel engines. The aim is to evaluate the engine dynamics, identify unknown …

Probability density evolution is the vital probabilistic information for stochastic dynamic
system. However, it may face big challenges when using numerical methods to solve the …

Owing to recent advancements in sensor technology, data mining, Machine Learning (ML)
and cloud computation, Structural Health Monitoring (SHM) based on a data-driven …

We present a novel methodology based on Physics-Informed Neural Networks (PINNs) for
solving systems of partial differential equations admitting discontinuous solutions. Our …

In this paper, we present and compare four methods to enforce Dirichlet boundary
conditions in Physics-Informed Neural Networks (PINNs) and Variational Physics-Informed …

Develo** constitutive models that can describe a complex fluid's response to an applied
stimulus has been one of the critical pursuits of rheologists. The complexity of the models …

The advancement of computational methods in structural engineering has significantly
benefited from the integration of machine learning techniques, particularly in solving …

Generative adversarial networks (GAN) have recently been used for a design synthesis of
mechanical shapes. A GAN sometimes outputs physically unreasonable shapes. For …