Despite the great promise of the physics-informed neural networks (PINNs) in solving
forward and inverse problems, several technical challenges are present as roadblocks for …

Recently, the advent of deep learning has spurred interest in the development of physics-
informed neural networks (PINN) for efficiently solving partial differential equations (PDEs) …

High-resolution (HR) information of fluid flows, although preferable, is usually less
accessible due to limited computational or experimental resources. In many cases, fluid data …

We present an approach for compressing volumetric scalar fields using implicit neural
representations. Our approach represents a scalar field as a learned function, wherein a …

With the development of deep learning (DL) techniques, many tasks in flow visualization that
used to rely on complex analysis algorithms now can be replaced by DL methods. We …

Since 2016, we have witnessed the tremendous growth of artificial intelligence+
visualization (AI+ VIS) research. However, existing survey articles on AI+ VIS focus on visual …

We analyze the applicability of convolutional neural network (CNN) architectures for
downscaling of short‐range forecasts of near‐surface winds on extended spatial domains …

We present STNet, an end-to-end generative framework that synthesizes spatiotemporal
super-resolution volumes with high fidelity for time-varying data. STNet includes two …

This paper investigates the application of physics-informed neural networks (PINNs) to solve
free-surface flow problems governed by the 2D shallow water equations (SWEs). Two types …

Despite the potential of neural scene representations to effectively compress 3D scalar fields
at high reconstruction quality, the computational complexity of the training and data …