The future development of quantum technologies relies on creating and manipulating
quantum systems of increasing complexity, with key applications in computation, simulation …

Physics-Informed Neural Networks (PINN) are algorithms from deep learning leveraging
physical laws by including partial differential equations together with a respective set of …

Engineering-scale problems generally can be described by partial differential equations
(PDEs) or ordinary differential equations (ODEs). Analytical, semi-analytical and numerical …

A physics-informed neural network (PINN) uses physics-augmented loss functions, eg,
incorporating the residual term from governing partial differential equations (PDEs), to …

Abstract Physics-informed Neural Networks (PINNs) have recently achieved remarkable
progress in solving Partial Differential Equations (PDEs) in various fields by minimizing a …

This paper presents an overview of scientific modeling and discusses the complementary
strengths and weaknesses of ML methods for scientific modeling in comparison to process …

When PINNs solve the Navier–Stokes equations, the loss function has a gradient imbalance
problem during training. It is one of the reasons why the efficiency of PINNs is limited. This …

Predicting microscale mechanisms of plant-based food materials has been an enduring
challenge due to the inherent complexity of involved physics and prohibitively-high …

An outstanding amount of funds are allocated to maintain road network conditions. To
ensure the serviceability of roads, the accurate prediction of its roughness or International …

Application of physics-informed neural network (PINN) on neutron diffusion equation, which
is of great engineering significance for reactor physics field, has not received much attention …