Recent breakthroughs in computing power have made it feasible to use machine learning
and deep learning to advance scientific computing in many fields, including fluid mechanics …

Advancements in computing power have recently made it possible to utilize machine
learning and deep learning to push scientific computing forward in a range of disciplines …

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

This paper presents the fundamentals of Physics Informed Neural Networks (PINNs) and
reviews literature on the methodology and application of PINNs. PINNs are universal …

This work presents a physics-informed neural network (PINN) based framework to model the
strain-rate and temperature dependence of the deformation fields in elastic-viscoplastic …

Physics informed neural networks (PINNs) have emerged as a powerful tool to provide
robust and accurate approximations of solutions to partial differential equations (PDEs) …

In Computational Fluid Dynamics (CFD), coarse mesh simulations offer computational
efficiency but often lack precision. Applying conventional super-resolution to these …

This work presents a physics-informed deep learning-based super-resolution framework to
enhance the spatio-temporal resolution of the solution of time-dependent partial differential …

This study proposes an innovative deep learning-aided approach based on generative
adversarial networks named AttGAN, which is specialized for solving the spatio-temporal …

In industrial measurement, temperature field measurement typically relies on thermocouples
and spectroscopic techniques. These traditional methods often suffer from insufficient …