Deep learning (DL) has driven innovation in the field of computational imaging. One of its
bottlenecks is unavailable or insufficient training data. This article reviews an emerging …

In recent years, a plethora of methods combining neural networks and partial differential
equations have been developed. A widely known example are physics-informed neural …

Data-driven discovery of governing equations in computational science has emerged as a
new paradigm for obtaining accurate physical models and as a possible alternative to …

Elasticity imaging is a technique that discovers the spatial distribution of mechanical
properties of tissue using deformation and force measurements under various loading …

Hemodynamic parameters are of great significance in the clinical diagnosis and treatment of
cardiovascular diseases. However, noninvasive, real-time and accurate acquisition of …

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

In this work, a radial basis function differential quadrature-based physics-informed neural
network (RBFDQ-PINN) is proposed to simulate steady incompressible flows. The …

Vascular flow modelling can improve our understanding of vascular pathologies and aid in
developing safe and effective medical devices. Vascular flow models typically involve …

Introduction Sub-concussive head impacts in soccer are drawing increasing research
attention regarding their acute and long-term effects as players may experience thousands …

Sea surface temperature (SST) prediction has attracted increasing attention, due to its
crucial role in understanding the Earth's climate and ocean system. Existing SST prediction …