There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …

The reliable identification of subsurface sedimentary structures (ie, geologic heterogeneity)
is critical in various earth and environmental sciences, petroleum reservoir engineering, and …

Here we propose a generalized space-time domain decomposition approach for the physics-
informed neural networks (PINNs) to solve nonlinear partial differential equations (PDEs) on …

Recent advances of data-driven machine learning have revolutionized fields like computer
vision, reinforcement learning, and many scientific and engineering domains. In many real …

There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …

Recently, the advent of deep learning has spurred interest in the development of physics-
informed neural networks (PINN) for efficiently solving partial differential equations (PDEs) …

With the advantages of fast calculating speed and high precision, the physics-informed
neural network method opens up a new approach for numerically solving nonlinear partial …

The incorporation of physical information in machine learning frameworks is opening and
transforming many application domains. Here the learning process is augmented through …

Over the last few decades, existing Partial Differential Equation (PDE) solvers have
demonstrated a tremendous success in solving complex, non-linear PDEs. Although …

Physics-informed neural networks (PINNs) are an emerging method for solving partial
differential equations (PDEs) and have been widely applied in the field of scientific …