Abstract Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode
model equations, like Partial Differential Equations (PDE), as a component of the neural …

Data-driven science and technology offer transformative tools and methods to science. This
review article highlights the latest development and progress in the interdisciplinary field of …

While the popularity of physics-informed neural networks (PINNs) is steadily rising, to this
date PINNs have not been successful in simulating dynamical systems whose solution …

Physics-informed neural networks (PINNs) have been popularized as a deep learning
framework that can seamlessly synthesize observational data and partial differential …

Physics-informed neural networks (PINNs) as a means of solving partial differential
equations (PDE) have garnered much attention in the Computational Science and …

In this paper, a multi-layer feed-forward deep neural network was introduced into the
plasma/plasma catalysis kinetics modeling. The deep learning-assisted modeling enables …

In recent years, a plethora of methods combining neural networks and partial differential
equations have been developed. A widely known example are physics-informed neural …

We present a new Integrated Finite Element Neural Network framework (I-FENN), with the
objective to accelerate the numerical solution of nonlinear computational mechanics …

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 …

Tumors acquire alterations in oncogenes and tumor suppressor genes in an adaptive walk
through the fitness landscape of tumorigenesis. However, the interactions between …