We introduce multi-head neural networks (MH-NNs) to physics-informed machine learning,
which is a type of neural networks (NNs) with all nonlinear hidden layers as the body and …

We propose a physics-informed learning based on variational autoencoder (VAE) to solve
data-driven stochastic differential equations when the governing equation is known and a …

Structural system identification is critical in resilience assessments and structural health
monitoring, especially following natural hazards. Among the nonlinear structural behaviors …

We introduce a new class of spatially stochastic physics and data informed deep latent
models for parametric partial differential equations (PDEs) which operate through scalable …

The discovery of governing differential equations from data is an open frontier in machine
learning. The sparse identification of nonlinear dynamics (SINDy)\citep …

We introduce a physics-driven deep latent variable model (PDDLVM) to learn
simultaneously parameter-to-solution (forward) and solution-to-parameter (inverse) maps of …

This study employs a data-driven approach to studying physical system vibrations, focusing
on two main aspects: using variational autoencoders (VAEs) to generate physical data (ie …

The physics-informed neural network (PINN) has received much attention in the field of
partial differential equation (PDE) solving due to its adaptability to different governing …

Electromagnetic relays (EMRs) are intricate micro-electromechanical systems characterized
by nonlinear behavior and coupling effects between electromagnetic and mechanical forces …

In recent engineering applications using deep learning, physics-informed neural network
(PINN) is a new development as it can exploit the underlying physics of engineering …