Physics-informed neural networks (PINNs) have shown to be effective tools for solving both
forward and inverse problems of partial differential equations (PDEs). PINNs embed the …

The rapid growth of deep learning research, including within the field of computational
mechanics, has resulted in an extensive and diverse body of literature. To help researchers …

Pre-trained machine learning (ML) models have shown great performance for awide range
of applications, in particular in natural language processing (NLP) and computer vision (CV) …

This review focuses on recent advancements in data-driven methods for analyzing flow and
transport in porous media, which are showing promising potential for applications in energy …

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 …

The physics-informed neural network (PINN) is a general deep learning framework for
simulating flows with limited or no labeled data. In the current study, we develop a physics …

Physics-informed neural networks (PINNs) incorporate physical knowledge from the
problem domain as a soft constraint on the loss function, but recent work has shown that this …

Physics-informed neural networks (PINNs) have shown remarkable prospects in solving the
forward and inverse problems involving partial differential equations (PDEs). The method …

Simulating solute transport in heterogeneous porous media poses computational
challenges due to the high-resolution meshing required for traditional solvers. To overcome …

The Physics-informed neural network method (PINN) has shown promise in resolving
unknown physical fields in solid mechanics, owing to its success in solving various partial …