Uncovering the mechanisms of physics is driving a new paradigm in artificial intelligence
(AI) discovery. Today, physics has enabled us to understand the AI paradigm in a wide …

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

In various engineering and applied science applications, repetitive numerical simulations of
partial differential equations (PDEs) for varying input parameters are often required (eg …

Transfer learning (TL), which enables neural networks (NNs) to generalize out-of-distribution
via targeted re-training, is becoming a powerful tool in scientific machine learning (ML) …

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

Training nonlinear parametrizations such as deep neural networks to numerically
approximate solutions of partial differential equations is often based on minimizing a loss …

Modeling fluid flow and transport in heterogeneous systems is often challenged by unknown
parameters that vary in space. In inverse modeling, measurement data are used to estimate …

Physics-informed machine learning (PIML) as a means of solving partial differential
equations (PDEs) has garnered much attention in the Computational Science and …

In this work, we have applied physics-informed neural networks (PINN) for solving mesh
deformation problems. We used the collocation PINN method to capture the new positions of …

The application of physics-informed neural networks (PINNs) to address problems involving
partial differential equations (PDEs) is increasing. However, PINNs still need lots of …